Track B

Grab continues to put unwavering effort into enhancing the quality of OpenStreetMap data in South-East Asia, leveraging abundant local resources and dedicated country-specific mapping teams. The latest development in this ambitious venture is the forthcoming addition of a real-time incident reporting feature. Here, Grab harnesses reports from driver partners to fine-tune the map data further.

Although we are only at the inception of this initiative, we’re already receiving a significant number of reports addressing the “Turn Restriction”, “One-way”, “Gate Closed”, and “Incorrect Street Name” related map data discrepancies. These reports will pinpoint the exact or nearby location of the reported issue, as relayed by drivers in real-time or when taking detours, along with the reporting date. So, we are using our location resource to verify these and fix the map data issue in OpenStreetMap using available resources.

We believe that this data holds immense potential if harnessed collectively through community participation, paving the way to a comprehensive solution. On this note, in the future, Grab aims to make accessible the reported data for review by the community.

Grab is going to open real-time incident reports to the community to fix the map data issue on OpenStreetMap.

Advances in weather forecasting and climate science now enable to act before hazards strike rather than investing primarily in humanitarian response after disasters happen. These anticipatory approaches enable more people to receive needed assistance ahead of predictable shocks. Anticipatory action allows humanitarians and affected communities to make informed decisions ahead of a humanitarian crisis – saving time and money; preventing displacement, disease, loss of livelihood; and preserving the dignity of those affected. The availability of accurate information is an integral part in the anticipatory action to take proper decisions within a short lead time period. Disasters has a spatial dimension too. Hence, an integrating spatial dimension in to the data and information is a must to make effective and efficient decisions during humanitarian action. Sri Lanka Anticipatory Action for Disaster Mitigation Project integrated Open Street Mapping tool to create a spatial database. This is prepared with the support of relevant stakeholders. This has enabled to analysis of spatial distribution of vulnerable households across the project locations. This helped decision makers to mobilize more resources to the needy areas during humanitarian action.


Key words: Anticipatory Action, Open Mapping, Data and Information, Resource Mobilization

Learners will get hands-on experience using OpenStreetMap data for disaster resilience mapping during this 2-hour course. The talk will begin with a brief overview of OpenStreetMap and how this free and open-source geospatial dataset is being used in many impact areas throughout the world. They would subsequently be given the opportunity to brush up on their knowledge of the QGIS interface. Following that, the QGIS plugins will be introduced, and learners will be able to test them out using OpenStreetMap sample datasets. The ultimate goal is to assist learners in understanding the use of open mapping data and tools like OSM and QGIS in various stages of disaster management.

Urbanization presents opportunities and challenges, with slum areas being a critical concern in many rapidly growing cities. Accurate identification and prediction of slum areas are imperative for effective urban planning and resource allocation. We propose using OpenStreetMap (OSM) Building data to identify slum areas in regions where slum datasets are lacking. The proposed methodology incorporates variable extraction using the “momepy” library to identify and characterize buildings effectively.

In this study, we employ a two-stage process. Firstly, we utilize the momepy library to extract pertinent variables that encapsulate geometric and topological attributes of buildings from OSM data. These variables serve as inputs to our machine-learning model. Secondly, a predictive model is developed using machine learning algorithms for regions with known slum areas. This model is then employed to predict potential slum areas in regions with insufficient or lacking slum-related data.

Preliminary experiments conducted on real-world datasets demonstrate the effectiveness of our approach. The results underscore the utility of OSM data in slum area detection, showcasing its capacity to bridge data gaps and facilitate proactive urban planning. Furthermore, the proposed approach offers adaptability to various urban contexts, making it a valuable tool for decision-makers and researchers in diverse regions. Additionally, the study contributes to ongoing discussions regarding the reliability of OSM data, especially in challenging urban environments like slums.

The YouthMappers Regional Validation Hub Formulation aims to establish a centralized hub for validating mapping data generated by YouthMappers chapters in the region. This is to ensure the accuracy and quality of the mapping outputs before they are endorsed to the YouthMappers Global Validation Hub.

The Regional Validation Hub Formulation emphasizes the importance of maintaining data integrity and quality within the YouthMappers network. By establishing a regional hub, YouthMappers in Asia can benefit from a centralized platform for validation, ensuring that their mapping efforts contribute to reliable and impactful geospatial data.

The purpose of this workshop is to teach the validation tools of JOSM to the prospective validators in Asia-Pacific. The workshop will cover the following topics: configuring JOSM, fixing common errors and warnings, and getting feedback on proper validation techniques.