Tag: deepVGI

  • Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection

    Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common approach to securing clean water supplies for this purpose is via wastewater treatment. To date, […]

  • Where to map in OpenStreetMap next? Experiences from Mozambique, India, and Tonga

    OpenStreetMap (OSM) has been intensively used to support humanitarian aid activities, especially in the Global South. Its data availability in the Global South has been greatly improved via recent humanitarian mapping campaigns and due to the efforts of local communities. However, large rural areas are still incompletely mapped. The timely provision of map data is […]

  • Automatic building detection with ohsome2label and Tensorflow

    Accurate and complete geographic data of human settlement is crucial for humanitarian aid and disaster response. OpenStreetMap (OSM) can serve as a valuable source, especially for global south countries where buildings are largely unmapped. In a previous blog, we introduced our recent work in detecting OpenStreetMap missing buildings, so this time we will show you […]

  • Detecting OpenStreetMap missing buildings by transferring pre-trained deep neural networks

    Recently, a new research paper “Detecting OpenStreetMap missing buildings by transferring pre-trained deep neural networks” (Pisl, J., Li, H., Herfort, B., Lautenbach, S., Zipf, A. 2021) has been accepted at the the 24th AGILE conference 2021. The conference will take place virually on June 8 to 11, 2021. Accurate and complete geographic data of human […]

  • Introducing ohsome2label tool to generate training samples from OpenStreetMap for geospatial deep learning

    After more than a decade of rapid development of volunteered geographic information (VGI), VGI has already become one of the most important research topics in the GIScience community. Almost in the meantime, we have witnessed the ever-fast growth of geospatial deep learning technologies to develop smart GIServices and to address remote sensing tasks, for instance, […]

  • New DFG project: IdealVGI – Deep Learning with OSM

    Recently a new DFG project proposal was accepted to the GIScience Research Group Heidelberg within the DFG priority programme VisVGI (Volunteered Geographic Information: Interpretation, Visualisation and Social Computing” [SPP 1894]). It is joint collaboration project together with Prof. Begüm Demir from TU Berlin. IDEAL-VGI: Information Discovery from Big Earth Observation Data Archives by Learning from […]

  • Tools for Disaster Risk Reduction by HeiGIT – Celebrating the International Day for Disaster Risk Reduction

    Today we celebrate the International Day for #DisasterRiskReduction. HeiGIT offers a growing set of tools and services that support humanitarian aid during and before disasters. Examples include work in the context of the Missing Maps initiative, like conceptualising and extending microtasking apps like MapSwipe, as well as services for analysing MapSwipe data and making it […]

  • Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning

    Our new paper on Machine Learning and Humanitarian Mapping Nowadays, Machine Learning and Deep Learning approaches are steadily gaining popularity within the humanitarian (mapping) community. New tools such as the ML Enabler or the rapId editor might change the way crowdsourced data is produced in the future. Hence, at the Heidelberg Institute for Geoinformation Technology […]

  • Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks

    Recently a new paper about Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks (DNNs) has been presented at the AGILE GIScience conference 2019 in Cyprus. Although built-up areas cover only a small proportion of the earth’s surface, these areas are closely tied to most of the world’s population and the economic output, which makes […]

  • MapSwipe for Change Detection Analysis

    The Humanitarian OpenStreetMap Team (HOT) , the Heidelberg Institute of Geoinformation Technology (HeiGIT) , and the wider MapSwipe Community started working on an MapSwipe extension to monitor changes in satellite imagery. The goal of the two-month project is to extend the app with new functionalities that would allow the users to compare two satellite images […]

  • Empower Humanitarian Mapping with Deep Neural Networks to Detect Human Settlements

    Recently, earth observation by satellites has shown great capability in supporting a range of challenges such as disaster assessment, agriculture monitoring, and humanitarian mapping. MapSwipe, as a humanitarian mapping app, provides a crowdsourcing platform to collect volunteered geographical information (VGI), in order to generate the demanding base map of human settlements for better planning of […]

  • Put the world’s most vulnerable people on the map with MapSwipe

    Humanitarian organizations can’t help people if they can’t find them. This was the simple reason to create MapSwipe back in 2016 and it is still as pressing as in the very beginning. In the last 2,5 years volunteers have contributed more than 18,000,000 results, which help humanitarian organizations to create maps of human settlements and […]