OpenDRI brings the philosophies and practices of the global open data movement to the challenges of reducing vulnerability and building resilience to natural hazards and the impacts of climate change across the globe.


The Open Cities AI Challenge

Post by Dave Luo, Grace Doherty, and Nicholas Jones, GFDRR Labs/World Bank This article was originally published on Towards Data Science. Takeaways The Global Facility for Disaster Reduction and Recovery (GFDRR) is partnering with Azavea and DrivenData to introduce a new dataset and machine learning (ML) competition ($15,000 in total prizes) to improve mapping for resilient urban planning. Better ML-supported mapping for disaster risk management means addressing barriers to applying ML in African urban environments and adopting best practices in geospatial data preparation to enable easier ML usage. The competition dataset — over 400 square kilometers of high-resolution drone imagery and 790K building footprints — is sourced from locally validated, open source community mapping efforts from 10+ urban areas across Africa. Prize-winning solutions will be published as open-source tools for continued ML development and benchmarking. The Open Cities AI Challenge has two participation tracks: $12,000 in prizes for best open-source semantic segmentation of building footprints from drone imagery that can generalize across a diverse range of African urban environments, spatial resolutions, and imaging conditions.$3,000 in prizes for thoughtful explorations of Responsible AI development and application for disaster risk management. How might we improve the creation and use of ML systems to mitigate biases, promote fair and ethical use, inform decision-making with clarity, and make safeguards to protect users and end-beneficiaries? The competition is ongoing and ends March 16th, 2020. Join today! Open Data for Resilient Urban Planning Cities around the world are growing rapidly, especially in Africa — by 2030, half of Sub-Saharan Africa’s population will live in urban areas. As urban populations grow, their exposure to flooding, erosion, earthquakes, coastal storms, and other hazards becomes a complex challenge for urban planning. Understanding how assets and people are vulnerable to these risks requires detailed, up-to-date geographic data of the built environment. For example, a building’s particular location, shape, and construction style can tell us whether it will be more exposed to earthquake or wind damage than nearby buildings. Roads, buildings, and critical infrastructure need to be mapped frequently, accurately, and in detail if we are to understand and manage risk effectively. But in countries with less developed data infrastructure, traditional urban data collection methods can’t keep up with increasing density and sprawl. A field mapper from Open Cities Accra observes standing water and refuse in a flood-prone neighborhood of Accra, Ghana. Photo courtesy of Gabriel Joe Amuzu, Amuzujoe Photography. Thankfully, collaborative and open data collection practices are reshaping the way we map cities. Today, local mapping communities are improving maps for some of the world’s most vulnerable neighborhoods — bringing highly accurate and detailed geographic data up-to-date and to scale. GFDRR at the World Bank supports programs like Open Cities Africa and Dar Ramani Huria to map buildings, roads, drainage networks and more in over a dozen African cities, and Zanzibar Mapping Initiative was the world’s largest aerial mapping exercise using consumer drones and local mappers to produce open spatial data for conservation and development in the archipelago. To-date, OpenStreetMap contributors have mapped more than 70 million ways and 600 million nodes onto the African continent. Data collected in these community mapping programs are used to design tools and products that support government decision-making. Digitized maps are published to OpenStreetMap and aerial imagery to OpenAerialMap where they serve as data public goods that can be used and improved by all. The open source philosophy behind the movement and an emphasis on local skill-building has fostered local networks of talent in digital cartography, robotics, software development, and data science. Potential of Machine Learning for Mapping Advances in ML for visual tasks could further improve mapping quality, speed, and cost. Recent examples of ML applications for mapping include Facebook’s AI-assisted mapping tool for OpenStreetMap and Microsoft’s country-scale automated building footprint extraction (in USA, Canada, Tanzania and Uganda). Competitions like SpaceNet and xView2 advance ML practices for automated mapping of roads, buildings, and building damage assessment after disasters. Obstacles, however, stand in the way of effectively applying current ML mapping solutions to the African disaster risk management context. Africa’s urban environments differ significantly in make-up and appearance from European, American, or Asian cities which have more abundant data that ML models are often trained on. Buildings that are more densely situated and diverse in shape, construction style, and size may be less recognizable to ML models that saw few or no such examples in their training. Comparing urban built environments of Las Vegas, USA (left) to Monrovia, Liberia (right) at the same visual scale. Imagery courtesy of Microsoft Bing Maps and Maxar (DigitalGlobe) Imagery is collected by commercial drones at much higher resolution under diverse environmental conditions, requiring adaptation of models usually trained on lower-resolution, more consistently collected and preprocessed satellite imagery. Comparing urban details at typical satellite image resolution (>30cm/pixel, top) to drone/aerial image resolution (3–20cm/pixel, bottom) in Dar es Salaam, Tanzania. Imagery courtesy of Maxar and OpenAerialMap. Crowdsourced and community-driven data labeling may differ greatly in what base imagery layers are used, workflow, data schema, and quality control, requiring models that are robust to more label noise. Quality of hand-drawn building footprint labels (alignment and completeness) can vary across or within image scenes. Examples from Challenge training dataset for Kampala, Uganda (left) and Kinshasa, DRC (right). Geospatial data comes in a diversity of file formats, sizes, and schemas that create high adoption and knowledge barriers that hamper their use in machine learning. There is now a growing abundance of locally-validated open map data and high resolution drone imagery in diverse built environments. How might we best address these obstacles and enhance the state of practice in machine learning to support mapping for urban development and risk reduction for Africa’s cities? Introducing the Open Cities AI Challenge Dataset Working with partners Azavea and DrivenData, the Labs team at GFDRR combined the excellent work of many participatory mapping communities across Africa, applied best practices in cloud-native geospatial data processing (i.e. using Cloud-Optimized GeoTIFFs [COG] and SpatioTemporal Asset Catalogs [STAC]), and standardized wherever possible to make data more readily usable for machine learning. The result is a novel, extensive, open dataset of over 790K building footprints and 400 square kilometers of drone imagery representing 10 diverse African urban areas in ML-ready form. Comparing hand-labeled building footprints overlaid on drone imagery for 10 African urban areas included in the Challenge training dataset. Using COG and STAC for geospatial data provides us with bandwidth-efficient, rapid, and query-able access to our imagery and labels in a standardized format. Ease of access to files and indexing of data catalogs is particularly important for geospatial data which can quickly grow to 100s of gigabytes. It also enables us to tap into the growing ecosystem of COG and STAC tools, like STAC Browser to rapidly visualize and access any training data asset in a web browser, despite individual image files being up to several GBs and the entire dataset totaling over 70 GBs in size: Animated demo of using STAC Browser to visualize Challenge training data collections and assets . PySTAC, a new Python library by Azavea, enables STAC users to load, traverse, access, and manipulate data within catalogs programmatically. For example, reading a STAC catalog: train1_cat = Catalog.from_file('') train1_cat.describe()* <Catalog id=train_tier_1> * <Collection id=acc> * <Item id=665946> * <LabelItem id=665946-labels> * <Item id=a42435> * <LabelItem id=a42435-labels> * <Item id=ca041a> * <LabelItem id=ca041a-labels> * <Item id=d41d81> * <LabelItem id=d41d81-labels> * <Collection id=mon> * <Item id=401175> ... Inspecting an item’s metadata: one_item = train1_cat.get_child(id='acc').get_item(id='ca041a')one_item.to_dict(){ "assets": { "image": { "href": "", "title": "GeoTIFF", "type": "image/tiff; application=geotiff; profile=cloud-optimized" } }, "bbox": [ -0.22707525357332697, 5.585527399115482, -0.20581415249279408, 5.610742610987594 ], "collection": "acc", "geometry": { "coordinates": [ [ [ -0.2260939759101167, 5.607821019807083 ], ... [ -0.2260939759101167, 5.607821019807083 ] ] ], "type": "Polygon" }, "id": "ca041a", "links": [ { "href": "../collection.json", "rel": "collection", "type": "application/json" }, { "href": "", "rel": "self", "type": "application/json" }, { "href": "../../catalog.json", "rel": "root", "type": "application/json" }, { "href": "../collection.json", "rel": "parent", "type": "application/json" } ], "properties": { "area": "acc", "datetime": "2018-11-12 00:00:00Z", "license": "CC BY 4.0" }, "stac_version": "0.8.1", "type": "Feature"} Learn more about the dataset and STAC resources. Competition Accompanying the dataset is a competitive machine learning challenge with $15,000 in total prizes to encourage ML experts globally to develop more accurate, relevant, and readily usable open-source solutions to support mapping in African cities. There are 2 participation tracks: Semantic Segmentation track: $12,000 in prizes for the best open-source semantic segmentation models to map building footprints from aerial imagery. The machine learning objective is to segment (classify) every pixel in every image as building or no-building with model performance being evaluated with the Intersection-over-Union metric (aka Jaccard Index): Semantic segmentation is useful for mapping because its pixel-level outputs are relatively easy to visually interpret, verify, and use as-is (e.g. in the calculation of built-up surface area) or as inputs to downstream steps (e.g. first segment buildings and then classify attributes about each segmented building like its construction status or roof material). Segmentation track participants must also submit at least once to the Responsible AI track to qualify for $12,000 in segmentation track prizes. Example image chip (left) and segmentation (right) from the Challenge dataset. Responsible AI track: $3,000 in prizes will be awarded for best ideas applying an ethical lens to the design and use of ML systems for disaster risk management. ML can improve data applications in disaster risk management, especially when coupled with computer vision and geospatial technologies, by providing more accurate, faster, or lower-cost approaches to assessing risk. At the same time, we urgently need to develop a better understanding of the potential for negative or unintended consequences of their use. With growing attention given to questions of appropriate and ethical ML use for facial recognition, criminal justice, healthcare, and other domains, we have an immediate responsibility to elevate these questions for disaster risk. Examples of potential harm that ML technologies present in this space include, but are not limited to: Perpetuating and aggravating societal inequalities through the presence of biases throughout the machine learning development pipeline.Aggravating privacy and security concerns in Fragility, Conflict and Violence settings through combination of previously distinct datasets.Limiting opportunities for public participation in disaster risk management due to increased complexity of data products.Reducing the role of expert judgement in data and modeling tasks and in turn increasing probability of error or misuse.Inadequately communicating methods, results, or degrees of uncertainty, which increases the chance of misuse. ML practitioners and data scientists are uniquely positioned to examine and influence the ethical implications of our work. We ask challenge participants to consider the applied ethical issues that arise in designing and using ML systems for disaster risk management. How might we improve the creation and application of ML to mitigate biases, promote fair and ethical use, inform decision-making with clarity, and make safeguards to protect users and end-beneficiaries? This track’s submission format is flexible: participants can submit Jupyter notebooks, slides, blogs, essays, demos, product mockups, speculative fiction, art work, synthesis of research papers or original research, or whatever other format best suits you. Submissions will be evaluated by a panel of judges on thoughtfulness, relevance, innovation, and clarity. What Comes Next This challenge will produce new public goods that advance our state of practice in applying ML for understanding risk in urban Africa; this includes new ML performance benchmarks for building segmentation from aerial imagery in relevant geographies, top-performing solutions for mapping in African cities, and in-depth explorations of how we responsibly create and deploy AI systems for disaster risk management. Prize-winning solutions will be published as open-source tools and knowledge and the challenge dataset will remain an open data resource for continued ML development and benchmarking. GFDRR will use lessons learned to inform policies and procurement strategies for using ML for urban mapping and planning. Join the Challenge! The competition is currently running until March 16, 2020. With one month to go, there is plenty of time to explore the data and participate in either tracks but don’t delay, join today at:

How participatory mapping can make Brazzaville’s poor neighborhoods safer

Author: Dina Ranarifidy, World Bank Christ Mboungou, local cartographer from Moukoundzi-Ngouaka, says he wants to use the skills he gained to map climate risks in his neighborhood and develop better infrastructure. As you walk around Brazzaville, Republic of Congo, chances are that you will notice dapper-looking gentlemen and ladies in stylish and colorful attire. Striding through the streets of the capital city with pride, they are known as the Sapeurs. The presence of these Sapeurs showcasing their style might seem stark in contrast to their environment, which is often of infrastructure with low-quality social services in comparison. Putting looks and prestige before other needs may raise eyebrows, but the Sapeurs’ passion to distinguish themselves only clearly represents the lively and colorful aspirations of many people in the city, despite their unfortunate living conditions. Community participation is key to sustainably upgrading informal settlements To improve the urban environment for aspiring communities living in informal settlements in Brazzaville, the World Bank-financed Congo Urban Development and Poor Neighborhood Upgrading project (DURQuaP) has put the communities themselves at the forefront of decision making. To give communities a sense of worth and belonging, the project set up local development committees in target neighborhoods. Over 300 people—half of them women— were selected on a voluntary basis and received trainings on dynamics and leadership change to help them voice their needs, participate in community planning and implementation, and help strengthen community ownership. A neighborhood committee meeting in Moukoundzi Ngouaka in Brazzaville Upgrading informal settings through community mapping To mitigate adverse effects of flood risk – a daily threat in Brazzaville’s poor urban settlements –  engaging communities and valuing the knowledge of their own space is critical. Brazzaville, together with 11 other African cities, is taking part in the Africa Open Cities Initiative to engage local government, civil society, and the private sector to develop data needed to meet urban resilience challenges. This initiative feeds into the participatory approach that shapes the project’s design and complements DURQuaP efforts by helping communities take the lead in making their neighborhoods safer. The Open Cities teams have been working hand and hand with communities and technical leaders to get a better sense of their perception of risks, collect, analyze and map risk-related data to update the Open Street Map (OSM) database.  In Brazzaville, the methodology includes exploratory walks around neighborhoods and focus groups with residents who share their perception of the risks in their neighborhoods and living spaces. Interestingly, the exercise not only values and formalizes the knowledge of the community in their own territory, but also allows to differentiate the perception of risks by gender and generation, offering a wider span of data. The qualitative information gathered is then combined with quantitative data that is collected, also in a participatory manner. The project has also teamed up with the city of Brazzaville to integrate existing geospatial data on the neighborhoods and to make them available in Open Street Map. The data from community maps will directly inform the neighborhood upgrading plans that the DURQuaP project will finance, bolstering physical investments. Building human capital and bringing innovation to poor urban neighborhoods through data collection Community mapping activities have been critical to creating new skills for neighborhood residents. In this light, students from the Université Marien Ngouabi receive targeted training, technical support, and mentorship to compile open spatial data related to natural hazards and develop tools for stakeholders to utilize risk information. This empowerment of youth is expected to foster new vocations in cartography and urban planning – and promote the emergence of a more environmentally conscious generation of young Congolese. Additionally, innovative tools such as satellite and drone imagery provided up-to-date geographic data on the environment. Bintou Moussoyi, student at the University Marien Ngouabi, explains how the Open Cities field work exposed her to erosion, flood damages and houses buried under sand and made her better understand their impact on populations’ lives, in Open Cities workshop in Brazzaville in May 2019. Less climate risk, more resources to improve lives Through the neighborhood-focused approach, the project will impact the lives of people at a granular level by mitigating the climate risk they face today and – by extension, at a macroeconomic level – will contribute to the goal of reducing poverty and increasing the welfare of the poorest in society. The aim must be fewer losses and therefore more opportunities and resources the residents of Brazzaville can mobilize for their own benefit. Less avoidable risk to worry about will help everybody, not only the Sapeurs, focus on the more enterprising aspects of life. Yet, one thing is certain: the Sapeurs will use this opportunity the most creatively… Open Cities Africa is financed by the EU-funded ACP-EU Africa Disaster Risk Financing Program, and the implementation of the resilience-related activities within the DURQuaP is supported by the ACP-EU Natural Disaster Risk Reduction Program, both managed by the Global Facility for Disaster Reduction and Recovery. More information on the ACP-EU NDRR Program and the support it provides to the Republic of Congo can be found here. This piece was originally published on the World Bank Nasikiliza blog.

Capturing an archipelago: Open Cities Zanzibar

Author: Primož Kovačič , Spatial Collective Open Cities Africa Project Open Cities Africa is an initiative carried out in 10 cities in Sub-Saharan Africa, to engage local government, civil society, and the private sector to develop the information infrastructures necessary to meet 21st-century urban resilience challenges. Open Cities in Zanzibar is aligned with the Global Facility for Disaster Reduction and Recovery’s (GFDRR) Resilient Cities Program and is implemented through a unique partnership between GFDRR and the World Bank, city governments, and a partner community comprised of regional scientific and technology organizations, development partners, and technology companies to support upcoming or ongoing World Bank-supported activities in the selected cities. Zanzibar City For the purposes of disaster risk management, the Revolutionary Government of Zanzibar (RGoZ), specifically the Commission for Lands (COLA) and the Department of Urban Planning, have noted the need to update their Zanzibar Master Plan with locations of all the built structures and flood-prone areas in the city. Spatial Collective, the implementing partner of Open Cities Africa, spent many months on Zanzibar working with the local stakeholders and coordinating various data collection activities. The goal was to generate and visualize datasets critical to disaster risk management and to build the capacity of government staff, university students, and communities in the process. The main objectives of this project were to: Finalize the digitization of buildings on Unguja Island, the largest island of the Zanzibar Archipelago.Ensure that the dataset was of acceptable quality by the stakeholders.Assign building reference numbers to the entire dataset following very specific nomenclature put forth by the Zanzibar’s Commission for Lands.Carry out community mapping of amenities and a household survey in six of Zanzibar City’s shehias or wards.Provide a series of visualizations and in the process transfer knowledge to State University of Zanzibar (SUZA) and Zanzibar Commission for Lands (COLA) students and staff. Digitization Building upon the previous efforts, the main objective of the Open Cities Project on Zanzibar was to finalize the digitization of all the buildings on Unguja Island. For this reason, a digitization workspace with several computers and a localized network for accessing and sharing data was established at the Commission for Lands. A dozen former students from the Zanzibar State University used the workspace to digitize about 160,000 buildings, mostly in Zanzibar City. These buildings were added to the pre-existing dataset of 200,000 buildings, completing the digitization of Zanzibar Archipelago’s largest island. The entire dataset was re-checked for errors and building reference numbers were assigned following the nomenclature put forth by the Commission for Lands. Fieldwork After the digitization was complete, it was time for fieldwork. The aim was to collect data critical to the Commission for Lands’ urban planning efforts, specifically on flooding, waste management, and transportation. The stakeholders agreed to carry out a household survey and GPS data collection in six shehias or wards. Twenty former State University of Zanzibar students, several Commission for Lands staff, and community representatives from each shehia were trained and participated in mobile and GPS data collection. Altogether, 2,100 buildings were surveyed using Open Data Kit and ONA software. The data touched on registering building types, collecting basic demographic information, documenting people’s experiences of flooding, determining access to waste management services, and assessing transport patterns and habits of residents. GPS data collection of relevant amenities was also carried out in the area. At the end of each day, the data was sent to the Zanzibar’s Commission for Lands where it was stored and visualized. Capacity building Likely the most important effort in this project was directed to working with existing data communities on Zanzibar and building capacity of University students and the Commission for Lands staff. A dozen full-time digitizers were engaged between July 2018 and end of October 2018 and approximately 30 people were trained and participated in GPS and mobile data collection in January and February 2019. The trainees and data collectors were former SUZA students and COLA staff, community members, shehia administrators, and local emergency responders. At least 25 training events were at the Commission for Lands touching on QGIS essentials, quality control and quality assurance, and OpenStreetMap. To raise awareness about the project, half a dozen public events were held in and around Zanzibar City, and a large delegation from the Island attended both FOSS4G conference in Dar es Salaam and MapBox training on Zanzibar. In February 2019, the Open Cities Zanzibar team had the privilege to present its work to the Director of Social, Urban, Rural and Resilience Practice at the World Bank. Final Products Between July 2018 and February 2019, 160,000 buildings were digitized and added to the pre-existing dataset of 200,000 buildings on Unguja Island, completing the digitization of Zanzibar Archipelago’s largest island. The buildings were checked and corrected for errors and building reference numbers were assigned to each building. Several roads on Unguja Island and about 20,000 buildings on Pemba Island were also digitized. In February, a household survey of about 2,100 households and GPS mapping within 6 shehias was complete. An interactive map of the area and several printed maps were made openly available. All the drone imagery is also made available to the public under a creative commons license. Finally, a series of blog posts, social media posts, and manuals documenting and promoting Open Cities activities Zanzibar were written and shared with the public. Way forward Zanzibar has been through some sort of a renaissance of geospatial activities and open data in recent years. The whole archipelago was mapped using drones and hundreds of thousands of buildings were digitized just in the last two years. More importantly, dozens of youth worked on these projects, gaining crucial skills in mapping and other geospatial activities. To keep the positive momentum going, it is crucial to develop a working environment for these emerging data and technology communities on Zanzibar. The Islands needs something like an urban laboratory, where creative ideas could come to fruition. A place like that could support the government in its endeavors and perhaps, more importantly, find innovative ways to share the data back with the people. This piece was originally published on Mapping: (No) Big Deal.

Uganda Open Mapping for Resilience Completes Ggaba Parish Pilot

The Uganda Open Mapping for Resilience project team have completed fieldwork in Ggaba Parish, gathering important information for the lakeside communities to highlight and analyze disaster risk. This follows up on the capacity development on open mapping for resilience methodology across Ugandan authorities. This OpenDRI initiative has seen residents and GIS professionals collaborating shoulder to shoulder, using the technological and transparent culture of Open Data, connecting community engagement with government programming in this resource-poor setting. The free availability of these innovative tools is allowing communities a geospatial understanding of some of the risks they live with and equipping them to convey these risks to the authorities who serve them. Field data management has been embedded in the heart of Ggaba, headquartered in the local secondary school. Hazards and Resilience in Ggaba: 25-May 2018 Severe flash floods are a frequent occurrence in Kampala. Ggaba is no exception, and umbrellas are intrinsic to the field kit. Ggaba is a rapidly developing neighborhood inside the perimeter of Kampala. During (increasingly unpredictable) rainy seasons, Uganda’s capital is highly prone to flooding. This photo was taken not far from Ggaba. Although dramatic, it is not unexpected. The risks here are manifold and difficult to contain. And they usually go unseen outside the communities themselves. Informal settlements are growing across the city as Ugandans migrate to Kampala in search of government services and jobs. Katoogo village in Ggaba parish: Children routinely collect water in these communities, even swampy standing water Even the more permanent and formal settlement areas of this slice of the Ugandan capital Kampala are closely arranged and often prone to fire. Cooking – commercial and household- is done on wood and charcoal, usually under grass or wooden roofs. Disaster Risk Reduction happens on a ‘hyper-local’ basis in every household. People build their houses on legs, although they rot over time, or are eaten by termites. Even ‘damp-proof courses’ of waterproof plastic appear at spectacular heights in the walls of new-builds, to prevent the structural ingress of floodwaters. Waist-height damp-proof course from used plastic bags House walls also get built higher, people hand-carry hundreds of tons of earth to raise the ground, and causeways often get built out of rubble from ruined buildings, providing ways to get about the neighbourhood every day Ever-present swamp water seen to the left of the path. Data collection: Local engagement and knowledge taught our surveying teams many things over the weeks in the field. The team, made up of participants from a broadly-mixed background, included many local community members, staff from Kampala Capital City Authority (KCCA), Uganda Bureau of Statistics (UBOS), and Office of the Prime Minister’s Department of Disaster Preparedness and Management (OPM DDPM) also joined to participate, share and learn. NGO workers and YouthMapper volunteers also joined the team. Drain-Surveying Team at work in Ggaba parish. Using free Android apps OpenDataKit (ODK) and OpenMapKit (OMK), surveyors visited every building, drain, and relevant point of interest across four villages in Ggaba, collecting data on construction materials, height, use, and other relevant features. The remotely-mapped shapes of Ggaba houses, kitchens, businesses, and health centres, traced by OpenStreetMap remote mappers around the world, have now been given characteristics which can be measured, assessed, and improved. Local Knowledge: According to our field teams, the community cite the major cause of flooding in Ggaba to be heavy rainfall and surging  water levels in Lake Victoria. Since the implementation of multiple dams on the Nile River, commercial operators on the lake acknowledge that water levels locally have risen by more than a metre. Topological data from unmanned aerial vehicles (UAV) helps to model this hazzard, and coupled with meteorological data tracking, can be used predictively and preemptively to focus interventions at a new level of resolution previously unavailable to KCCA and partners. But flood and fire are not the only disaster hazards amongst the semi-transient population in this town which borders a lake common with Tanzania and Kenya. Resilience is seated, to a large extent, in knowledge and understanding. People frequently have to evacuate their homes in Ggaba. Many people claim that wind, as much as localised rainfall, is more of an local flooding factor. Like hurricane surges, low-lying areas get quickly inundated from storm winds. Life has an unpredictable quality in Ggaba. But these close-knit communities pull together valiantly, and things are improvised in the community. Local Networks: The local character of data being collected by the team is as important to the Humanitarian OpenStreetMap Team (HOT) methodology as the local technology it leverages. Modern smartphones contain GPS hardware accurate enough for cadastral-standard and are now commonly owned and used across Uganda and the continent. This local ownership of powerful technology has been helping “Community Mapping” campaigns to express community needs for some years. MapUganda and HOT have helped develop some of these campaigns over this time, and this project channeled that experience into the training of more than twenty people to apply these techniques, giving access to an unprecedented representation of community needs. Flood history points were collected on the ground using smartphone surveys, serving as an empirical baseline to show the trends in flooding in Ggaba. Points collected were in all areas locally known to experience flooding, with depth and locations specified. Points were also taken at the edge of swamps in Katoogo that are more prone to flooding, with some sample “deep-spots” further into the flood-prone areas. Village pathway in Katoogo village in Ggaba parish. Standing water is a common problem, and an incubator for malaria mosquitos and other diseases. Data cleaning was collaborative work with the Office of the Prime Minister, Uganda Bureau of Statistics, Kampala Capital City Authority, and the local community (with help from the Global HOT volunteer network). It is now appearing as detailed and searchable data on OpenStreetMap. This data, publicly available online, is also packaged and given to government stakeholders for purposes of planning and development. Data cleaning with MapUganda, UBOS, and YouthMappers Flood history: Made possible by our community field data: Flood data points / flood extent mapping UAV/Drone Data: Outputs such as the above heatmap were made possible by our fieldwork, but an important component of the Uganda Open Mapping Project is to collect visual and topographical data by UAV mapping. Common access to controlled and bespoke aerial data is a relatively recent occurence. The emerging possibilities arising from a combination of ground and aerial data collection are still being explored around the world. This project was an opportunity to examine how the two formats could complement each-other in the Kampala context. A week after the field mapping, government permission to fly the UAV was finally obtained, and much was learned from the application and negotiation process. While not the ideal order, it provided a great opportunity to engage once again the government and community for a mapathon to improve data based on drone imagery. Project partners GeoGecko flew the area of interest collecting high resolution imagery. UAV imagery lends other environmental dimensions to projects such as this, particularly when coupled with ground-truthed statistics. Unforeseen narratives can become apparent which are not seen through lower resolution satellite imagery. These can highlight elements of physical and structural risk which would not be cross-associated from a ground-based perspective alone. The imagery and 3D data collected adds context to a number of metrics: This is a rapidly-changing environment, and satellite imagery can be more than a year old. “Real-time” drone imagery allows us to update new structures that do not show on our satellite imagery.It can also allow us to identify buildings which have been dismantled/demolished since our satellite imagery.Currently-evolving technology includes 3D modelling/analysis to provide a view of the topography (shape of the ground), which can incorporate pinpointed field surveys from key informants about flood history in specific locations, to better model flood risk data. The combination of these and many other references enables an overall environmental understanding of the lakeside location, where winds and rains from far away can disrupt everyday existence abruptly, leading to previously unforeseen displacement and even deaths. Post-Field Mapathon The surveyors field work validated remote mapping from imagery available at the time. A new HOT Task was created to focus and guide the work, leading up to a mapathon hosted by KCCA at City Hall. There were 14 participants, spanning KCCA, UBOS, OPM, HOT, MapUganda, GeoGecko, and the Ggaba community. Modifying data during the 2nd Phase Mapathon using UAV imagery in JOSM In that sesion, we were privileged to be visited the Lord Mayor of Kampala, Erias Lukwago, who is a keen fan of GIS and was impressed with the work. In his words, he expressed the concern of the Katoogo residents facing the challenges of being displaced by the wetlands. The Lord Mayor of Kampala on the right looking on as Geoffrey (HOT) demonstrates mapping activity during the mapathon. Mapathon participants engaged with remote-mapping tracing. Hillary (MapUganda) talking through the procedure with participants. Mappers improved the buildings and roads in OSM from the new imagery. Results of the mapping by participant, a combination of mapathon and remote mapping. Beyond the mapathon, the Task facilitated crowdsourcing from mappers, volunteers, and hired personnel. Teams were actively involved in mapping and validation to provide quality baseline data that would be used by GeoGecko to produce a printed atlas highlighting the results. All OSM features in the area of interest were successfully aligned to the orthoimagery used for mapping the task. The MapUganda Website of Uganda Open Mapping for Resilience explains project activities in Ggaba. The MapUganda website briefly describes the project activities, aims, and goals. This supports the Uganda project website on the Open Cities Africa website. Map information/statistics After 2nd Phase UAV Mapathon (Four Villages) TypeDescriptionFeature CountbuildingsAll Buildings4014highwaysAll Highways338drainage<canal,drain,ditch, drain_point>357Drain Points<silt_trap, drain_bridge, begins, ends, etc>216 Printed Map Atlas: Upon completion of all of the data gathering, cleaning, and analysis, it became important to share the findings with parties that can use the knowledge to make the Ggaba community more resilient to disaster hazards. OSM is by default and open and published repository, where Ugandans can immediately use and add to the information we gathered. And the reports and lessons learned are enabling KCCA, UBOS, and OPM to continue these efforts in other parts of the country. For the local community in the Ggaba villages, many of those affected and charged with taking action are not often online. Nor do they possess the hardware and network resources for continual analysis and data communication. To help make the findings more accessible, the team developed a printed map atlas of the four Ggaba villages. The atlas will be shared with all partners in large-format A3 size, providing a tangible tool connecting their field surveying to the data that has been joined with existing OSM maps. The cover of the printed atlas. A PDF version is available for download. Feedback from stakeholders: Simon Peter Okello OPM: “The field mapping was such experience that made the team see the reality of the slum dwellers. I was shocked to map a permanent house with no access road. The data we collected is very useful in disaster response. The training prior to our field work was informative, easy, and interesting in the sense that we would go out for practicals. There should have been prior community sensitisation at door to door level, to reduce effort for the field surveyors and the community guides justifying the purpose of the field activity and security matters.” Phiona Munezero UBOS: “I have benefited from the project since the inception. Learning about OSM and QGIS really broadened my scope of thinking and GIS application for UBOS. I am glad that mapping was achieved successfully. The survey form was on point, however I feel that the same information would have been effectively used by the community if it had been developed in a second language because OSM is about communities. The building attribute is a great advantage to UBOS, we definitely using it to identify schools and hospitals in the AOI for our population statistics. The mapathon was a new concept of crowdsourcing that’s great. Patrick Ojirot KCCA: This was a brilliant project. It is definitely going to enhance our datasets with new updated imagery, and I was glad that the time frame of the field data collection was reasonably short. All the same it would have been comprehensive if the datasets would have not been limited to buildings, drainages, and POI. If the need was to address risk management and disaster preparedness, it would have been more advantageous to include flood points, fire prone points, and crime areas for a holistic approach. Mapping out other disasters / integration of other mapped out multi-hazard risks e.g (fire, crime, disease, accidents, etc.) that occur within Ggaba Parish and publishing them on open street map would have given the project a solid outfit..​ Benefits to our organisation: It built capacity of a number of staff in using open street mapping/open source technologies for mapping, downloading and sharing data.Increased in knowledge in DRR, and built networks with other government organisations, non-government organisations, and members from different countries through workshops.Learnt exchanged information with members of other African countries on what disasters affect our city / their cities, where they occur, their intensity, who they affect most, and what can be done to reduce them.Participated in the data collection and mapathon for buildings footprints and drainage in Ggaba Parish. Robert Kaaya, Local Council Chair (LC1) Ggaba Water: I believe the data collected for roads, buildings and drainage systems should be used for purposes of development so as to improve the standards of living in this area. I believe what we need to do is share this data with the whole community, even with those who are illiterate to benefit. Peninah Babirye, Community Mapper, Ggaba: It was a really nice opportunity for me to be part of this project, I was able to interact with experienced people and also gained new skills and experience in terms of data collection. It also helped in learning how to approach people in the community since we were explaining to them the purpose of our field exercise. What I believe we could have done was to inform the community in advance about our field activities in addition to visits to the LCs. Land Politics and Urban Encroachment – Findings Today, the spatial inequality and segregation of African cities hides the poor, who often live on small marginal lands. Ggaba is a prime example of an urban Kampala area populated with such informal settlements. Land ownership and tenure in Kampala is complicated and slow to change, so due to long periods of under-utilization of land, encroachers can appear on these unclaimed lands. Our fieldwork gave us evidence that Ggaba was originally subject to formal planning. However, migration into the city continues, the compounded problem of land-tenure processing bottlenecks snowballs. The factor of a swelling population, scrambling for affordable land, forces people into riskier and riskier areas, which are less and less fit for habitation. Reflections: We in the consortium believe that space is not a container of human activity, but an active force shaping human life. Space is socially produced and therefore can be socially changed. This field exercise was to empower the stakeholders KCCA, OPM, and UBOS to come up with solutions that will reduce their risks to flood and other natural hazards. It led to many other thoughts and ideas for further projects and initiatives, deriving from the diverse combination of perspectives with which we have had the privilege to engage.


Open Cities Africa

Carried out in 11 cities in Sub-Saharan Africa to engage local government, civil society, and the private sector to develop the information infrastructures necessary to meet 21st century urban resilience challenges. The project is implemented through a unique partnership between GFDRR and the World Bank, city governments across the continent, and a partner community comprised of regional scientific and technology organizations, development partners, and technology companies. WEBSITE COUNTERPARTSCITIES National and Provincial Ministries, Municipal Offices and Local Development Committees ACCRA, Ghana ANTANANARIVO, Madagascar BRAZZAVILLE, Republic of Congo KAMPALA, Uganda KINSHASA, Democratic Republic of Congo MONROVIA, Liberia NGAOUNDÉRÉ, Cameroon NIAMEY, Niger POINTE-NOIRE, Republic of Congo SAINT-LOUIS, Senegal SEYCHELLES ZANZIBAR CITY, Tanzania Overview As urban populations and vulnerability grow, managing urban growth in a way that fosters cities’ resilience to natural hazards and the impacts of climate change becomes a greater challenge that requires detailed, up-to-date geographic data of the built environment. Addressing this challenge requires innovative, open, and dynamic data collection and mapping processes that support management of urban growth and disaster risk. Success is often contingent on local capacities and networks to maintain and utilize risk information, enabling policy environments to support effective data management and sharing, and targeted tools that can help translate data into meaningful action. Building on the success of the global Open Data for Resilience Initiative, its work on Open Cities projects in South Asia, and GFDRR’s Code for Resilience, Open Cities Africa is carried out in 11 cities in Sub-Saharan Africa to engage local government, civil society, and the private sector to develop the information infrastructures necessary to meet 21st century urban resilience challenges. Following an application process, a small team of mappers, technologists, designers, and risk experts in each of the selected cities receive funding, targeted training, technical support, and mentorship throughout the year of work to: i) create and/or compile open spatial data on the built environment, critical infrastructure, and natural hazards; ii) develop targeted systems and tools to assist key stakeholders to utilize risk information; and iii) support local capacity-building and institutional development necessary for designing and implementing evidence-driven urban resilience interventions. Phases of Implementation 1. Plan and Assess In the first phase, Open Cities teams establish what data already exists and its openness, relevance and value. Project target area and data to collect are finalized. This phase is also when teams identify project partners and stakeholders to ensure that efforts are a participatory process. At the Open Cities Kick Off Meeting, teams meet with Open Cities leadership and the other Open Cities teams in their cohort and receive training on project components. 2. Map In this second phase, teams roll out the findings and data capture strategy developed in the first phase to address critical data gaps relevant to their specific Problem Statements. On the ground, teams coordinate field data collection according to the approach developed and agreed upon in consultation with project stakeholders. Depending on needs, tools for data collection may include smartphones or tablets, drones for the collection of high resolution imagery, or handheld GPS. As the project team is training team members to collect data for the project, efforts are made to develop, and/or strengthen the local OpenStreetMap community within the selected city working in partnership with local stakeholders. Project teams may hold trainings, mapathons, or community town halls in coordination with a local university, NGO or government counterparts. 3. Design In this third phase of the project, teams use the data collected in the Map Phase to design a tool or product to communicate the data to their stakeholders to support decision-making. Products vary widely depending on city context and may include a database and visualization tool, an atlas, a map series, or a mobile application. 4. Develop and Present In the final phase of the project, teams develop their tools/products and share results with targeted end user populations and other relevant stakeholders. Once final products are shared, teams work with project mentors and Open Cities Africa leadership to establish a sustainability plan and to explore opportunities for expansion or extension. This could include convening meetings with the World Bank, government counterparts, or the nongovernmental organization and donor communities. It may also include the development of concept notes, proposals or additional user research. Learn More More information about the project and team activities can be found on the Open Cities Africa site.


In Niger, the World Bank is supporting the Government reduce the vulnerability of populations at risk of flooding, while taking into account the requirements of community development and capacity building of national structures both at central and local level. DATA SHARING PLATFORM   COUNTERPART PGCR-DU (Projet de Gestion des Risques de Catastrophes et de Développement Urbain – Disaster Risk Management and Urban Development Project) NUMBER OF GEONODE LAYERS39 Understanding Niger’s Risks Despite its semi-arid climate, Niger is regularly stricken by floods that destroy housing, infrastructure and croplands everywhere in the country. While flood damages usually occur in the vicinity of permanent water bodies such as the Niger and Komadougou rivers, more and more damages and casualties have been reported as linked to intense precipitations and runoff in urban areas. Despite the recurrent losses, little is known about the number of people who are living in flood-prone areas or the value of properties at risk. Furthermore, the vast majority of stations in the meteorological and hydrological collection network does not have the ability to transmit data in real-time and therefore cannot be fully exploited in emergency situations. Collecting Data With the support of the World Bank, the PGRC-DU is supporting the Nigerien Ministry of water and sanitation to retrofit the hydrometric station network with new water level gauges with real-time data transmission capability. The new gauges will make hydrometric data collection more efficient and more reliable while allowing for a faster detection of flood risk. At the same time, the PGRC-DU is funding the collection of critical socio-economic information and building characteristics in all areas of Niamey (the capital of Niger) that are deemed vulnerable to floods. UAVs are being used to acquire high-resolution images of potentially flooded areas that would help better identify buildings characteristics and develop a Digital Terrain Model (DTM) with 10cm vertical resolution, which will help better predict water movement in the area. Sharing Data The collected hydrometric data will be available to selected users in an online portal, along with various other data sets from regional and global sources. Part of the data collected in Niamey is expected to contribute to the OpenStreetMap project. The rest of the data will be analyzed and converted into vulnerability maps and reports available to the public. Using Data It is expected that the network of real-time hydrometric stations will be used to feed a flood warning system that will provide authorities a better estimate of flood risk at any given time. The acquired DTM is being used to develop computer models that can simulate flood propagation in the city of Niamey and evaluate the effects of existing of planned flood protection infrastructures. Finally, the collected socio-economic data combined with flood simulations will provide decision-makers an accurate estimation of flood risk in terms of exposed populations and expected economic damages.


In Uganda, the World Bank is supporting the Government to develop improved access to drought risk related information and quicken the decision of scaling up disaster risk financing (DRF) mechanisms COUNTERPART National Emergency Coordination and Operations Center (NECOC) Project Overview In the context of the third Northern Uganda Social Action Fund Project (NUSAF III), the World Bank is supporting the Government of Uganda to develop improved access to drought risk related information and quicken the decision of scaling up disaster risk financing (DRF) mechanisms. The OpenDRI team is providing technical assistance to Uganda’s National Emergency Coordination and Operations Center (NECOC) in determining requirements for collecting, storing and analyzing satellite data used for monitoring drought conditions. Understanding Uganda’s Risk In recent years Uganda has been impacted by drought, with more than 10% of the population being at risk. The northern sub-region of Karamoja is one of the most severely hit, with a consequent increase in food insecurity. Currently the Government of Uganda (GoU) faces challenges in the collection and analysis of information upon which they can base a decision to respond and mitigate such risk. Without transparent, objective and timely data, times in mobilizing and financing responses can be delayed. Collecting Data The World Bank is supporting GoU to strengthen its disaster risk management strategy and response mechanisms. The current engagement looks to develop a more systematic, robust system for collecting, storing and analyzing drought risk related information to enable GoU to make more timely decisions. By retrieving satellite data systematically, NECOC will be able to analyze current crop and vegetation conditions with historic information, and quickly detect early warning signs of drought. Uganda has a vibrant OpenStreetMap community, which has been mapping the country since 2010. A pilot community mapping project funded by GFDRR with support from the Government of Belgium, is being conducted in the city of Kampala. Sharing Data The OpenDRI team provides support and advice to GoU in developing best practices for sharing and managing risk related information. Interoperability of data sources produced by various ministries and non-government organizations is critical to ensure timely access to data by NECOC and conduct effective drought risk analysis. A geospatial data sharing platform will be deployed by GoU to facilitate exchange of such critical information and adoption of data standards. Using Data A technical committee, composed of experts from the government and partner organizations, has agreed to use a satellite derived indicator known as Normalized Difference Vegetation Index (NDVI) as the primary dataset to inform decisions for triggering the disaster risk financing mechanism. Initially the system will be exclusively dedicated to monitoring drought risk in the northern sub-region of Karamoja. In the following years, it is expected to expand operations and cover other regions exposed to drought risk, integrating additional data sources which will become accessible thanks to improved data collection strategies and sharing mechanisms.


The Revolutionary Government of Zanzibar (RGoZ) with the support of the World Bank has been developing evidence-based and innovative solutions to better plan, mitigate, and prepare for natural disasters. Zanzibar is part of the Southwest Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) which seeks to address high vulnerability of the Southwest Indian Ocean Island States to disaster losses from catastrophes such as cyclones, floods, earthquakes and tsunamis. These threats are exacerbated by the effects of climate change, a growing population and increased economic impacts. DATA SHARING PLATFORM PROJECT PAGE ZAN SEA FACEBOOK PAGE   Understanding Zanzibar’s Risk Zanzibar’s disaster events are mainly related to rainfall, and both severe flooding and droughts have been experienced. Sharing Data Island Map: OpenStreetMap Data collected through SWIO RAFI activities will be shared on a GeoNode. The ZanSea GeoNode currently contains 42 maps and 102 layers of geospatial data for Zanzibar. Collecting Data The Zanzibar mapping initiative is creating a high resolution map of the islands of Zanzibar and Pemba, over 2300 square km, using low-cost drones instead of satellite images or manned planes. The Zanzibar Commission for Lands will use the maps for better planning, land tenure and environmental monitoring. Data is being collected in collaboration with the RGoZ. Using Data Data collected can be used for risk assessment and planning activities.

Pacific Islands: Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Timor-Leste, Tonga, Tuvalu, Vanuatu

Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) is a joint initiative of SOPAC/SPC, World Bank, and the Asian Development Bank with the financial support of the Government of Japan, the Global Facility for Disaster Reduction and Recovery (GFDRR) and the ACP-EU Natural Disaster Risk Reduction Programme, and technical support from AIR Worldwide, New Zealand GNS Science, Geoscience Australia, Pacific Disaster Center (PDC), OpenGeo and GFDRR Labs. DATA SHARING PLATFORM NUMBER OF LAYERS 522 Understanding Risks in Pacific Island Countries The Pacific Island Countries are highly exposed to the adverse effects of climate change and natural hazards, which can result in disasters affecting their economic, human, and physical environment and impacting their long-term development agenda. Since 1950, natural disasters have affected approximately 9.2 million people in the Pacific Region, causing 9,811 reported deaths. Sharing Data throughout the Pacific Islands Launched in December 2011, the Pacific Risk Information System enhances management and sharing of geospatial data within the Pacific community. The system enables the creation of a dynamic online community around risk data by piloting the integration of social web features with geospatial data management. Exposure, hazard, and risk maps for 15 Pacific Countries were produced as part of the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) 2 and are accessible through this platform as powerful visual tools for informing decision-makers, facilitating communication and education on disaster risk management. Thumbnail Image by Samoa Department of Foreign Affairs and Trade licensed under CC BY 2.0

Sri Lanka

The Disaster Management Centre of Sri Lanka (DMC) with the support of the World Bank has been developing the Open Data for Resilience Initiative (OpenDRI) to support evidence-based methods to better plan for, mitigate, and respond to natural disasters. COUNTERPART Disaster Management Centre, Ministry of Disaster Management NUMBER OF BUILDINGS MAPPED 130,564 with 8 attributes each ROADS MAPPED >1000 km   Understanding Sri Lanka’s Risks Since 2000, flood and drought events have cumulatively affected more than 13 million people across Sri Lanka. Regular flooding, drought, and landslides are natural hazards that threaten the long-term growth and development of the country. In Sri Lanka, nearly $500 million in unplanned expenditures resulting from flooding in 2010 and 2011 has strained government budgets and required reallocation from other planned development priorities. The impacts of these events are growing due to increased development and climate change, both of which put more assets at risk. Sharing Data To enable better disaster risk modeling, the Government of Sri Lanka partnered with GFDRR, UNDP and OCHA on the development of an OpenDRI program in November 2012. This branch of the initiative focused on the South Asia Region and was dubbed the Open Cities project. A component of the OpenDRI Open Cities mission in Sri Lanka was to collate data around hazards and exposure and prepare them to be uploaded into a GeoNode which serves as a disaster risk information platform. Working with the DMC, the National Survey Department, Department of the Census and Statistics, Nation Building Research Organization, Information and Communication Technology Agency, Department of Irrigation, several universities and the international partners, the OpenDRI team supported DMC with the aggregation of data that had been stored in static PDFs, old paper maps and several databases onto the GeoNode. The data on the GeoNode is currently available to authorized users in the OpenDRI network, in preparation for launch. This transitional state is typical for open data projects, as the partnership reviews data with the parties and affirms that it is ready for release to the open public. Some layers may restrict access only to authorized users. Collecting Data The project has also built technical capacity and awareness in Sri Lanka through training sessions on open data and crowdsourced mapping in Batticaloa city and Gampaha District. As a result of the Open Data for Resilience Initiative, government and academic volunteers have mapped over 130,000 buildings and 1000 kilometers of roadways on the crowdsourced OpenStreetMap database. This enables the country to plan ahead and be prepared for future disaster and climate risks. It also helps planning during disaster responses: the data was used to assess flooding impacts in real time and direct government resources during the May 2016 floods in Gampaha district.


At OpenDRI we are committed to increasing information that can empower individuals and their governments to reduce risk to natural hazards and climate change in their communities. We’ve compiled a database of relevant resources to share what we have learned through our own projects and from the work of others.

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