chrischank/HOTOSM_OAM_codeV2

This pilot project is connected to a larger initiative to open-source the assisted mapping platform for Humanitarian OpenStreetMap Team based on Very High Resolution UAV imagery. The study test and evaluate multiple U-Net based architectures on building segmentation of Refugee Camps in East Africa. # Code produced for the academic abstract "Investigating the capability of UAV imagery for AI-assisted mapping of Refugee Camps in East Africa" This github repository is the code-base for the Master Thesis submitted for the Master der Naturwissenschaften in Applied Earth Observation and Geoanalysis of the Living Environment (EAGLE) at the Julius-Maximilians-Universität Würzburg. For Full Master thesis, please click here This work of this thesis is partnered with the Humanitarian OpenStreetMap (HOTOSM) and supported by the German Aerospace Center / Deutsches Zentrum für Luft- und Raumfahrt (DLR). ## Introduction HOTOSM would like to develop a solution for assisted mapping which can predict buildings in refugee camps on the drone imagery provided by associated organisation OpenAerialMap. Refugee camps and informal settlements reside some of the most vulnerable population, the majority of which are located in Sub-Saharan East Africa (UNHCR, 2016). Many of these settlements often lack up-to-date maps of which we take for granted in developed cities. Having up-to-date maps are important for assisting administration (e.g. population estimates, infrastructure development) in data impoverished environments and thereby encourages economic productivity (Herfort et al., 2021). The data inequality between developed and developing areas can be reduced using assisted mapping technology. To extract geospatial and imagery characteristics of dense urban enviornments, a combination of VHR satellite imagery and Machine Learning (ML) are commonly used. Recent advances in CV based Deep Learning might be able to address these issues. Convolutional Neural Networks (CNN) are a subtype of the Deep Learning (DL) family used in CV tasks. Past studies using CNN have shown high accuracy and transferability in small geographical setting (Kuffer et al., 2022). The datasets provided for this project consist of both highly structured, zoned newer refugee camps and chaotic, hi …