Im Mai 2023 besuchte Prof. Dr. Alexander Zipf gemeinsam mit Anne Schauß die UC Chile (Pontificia Universidad de Catolica de Chile) und das Heidelberg Center for Latin America (HCLA) in Santiago (Chile) und stellte in zwei öffentlichen Vorträgen vergangene und … Read More
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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 … Read More
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 … Read More
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 … Read More
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 … Read More
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 … Read More
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 … Read More
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 … Read More
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), … Read More
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 … Read More