This dataset supports the analysis and figures presented in the Global Urban OSM Building Completeness Analysis manuscript. It includes OSM completeness values and updated covariates for 13,189 urban agglomerations worldwide. The analysis employs a machine-learning model to infer the completeness of OSM building stock data, addressing the uneven spatial coverage of existing data.
Paper
This dataset introduces a fine-tuned model for detecting Municipal Solid Waste (MSW) using UAV imagery. The model was evaluated in the Msimbazi delta in Dar es Salaam, Tanzania, achieving an F1 score of 0.92. The generated MSW pile map revealed significantly higher contamination levels in the Msimbazi River bed compared to surrounding areas.
This dataset focuses on water tank detection to support vector control of infectious diseases transmitted by Aedes Aegypti. Using a semi-supervised self-training approach based on open satellite imagery, the study mapped water tank density in Rio de Janeiro. A negative binomial generalized linear regression model evaluated the statistical significance of water tank density for modeling Aedes Aegypti distribution from 2019 to 2021. The semi-supervised model outperformed a supervised model by 22% in F1-score.
License
OSMLanduse data is a scientific dataset generated within the scope of the Horizon 2020 – LandSense project. It is a classification of Sentinel-2 imagery using a deep learning model trained on OSM landuse and landcover features. The data might contain errorneous classifications. The classification values in the raster dataset correspond to a subset of the well-known CORINE LandCover Classification.
Free geoinformation, methods and software for the benefit of the environment and society.
Core-funded by
Background imagery created with data from OpenStreetMap contributors