OpenEarthMap Updates

OpenEarthMap meets OpenStreetMap

Researchers have introduced a transformative approach in a study published in IEEE TGRS, using OpenEarthMap alongside OpenStreetMap data to advance land-cover change detection. Their novel object-guided Transformer (ObjFormer) seamlessly integrates object-based image analysis with advanced Transformer architecture, drastically reducing computational overhead without additional parameters. The study pioneers a semi-supervised semantic change detection task, eliminating the need for manually annotated labels and improving scalability. The code and dataset have been open-sourced and are available here.

H. Chen, C. Lan, J. Song, C. Broni-Bediako, J. Xia, and N. Yokoya, ”Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer,” IEEE Transactions on Geoscience and Remote Sensing, 2024.

OpenEarthMap Few-Shot Challenge @ L3D-IVU CVPR 2024 Workshop

The OpenEarthMap Few-Shot Challenge, as part of the 3rd Workshop on Learning with Limited Labelled Data (L3D-IVU) at CVPR 2024, aims to advance few-shot semantic segmentation in remote sensing. It focuses on developing algorithms that require minimal labeled data, addressing key remote sensing challenges in disaster response, urban planning, and resource management. Top submissions will be presented at the CVPR 2024 Workshops and contribute to AI research for social good. More details can be found here.

OpenEarthMap Japan: 1st national-scale submeter-level land cover map.

OpenEarthMap Japan unveils the first national-scale submeter-level land cover map, revolutionizing the way we understand Japan's diverse landscapes. This pioneering project integrates minimal additional labeled data with the extensive OpenEarthMap dataset. Highly accurate land cover classification maps across the entire country, achieving an impressive 80% overall accuracy. The map is accessible here.

N. Yokoya, J. Xia, and C. Broni-Bediako, ”Submeter-level land cover mapping of Japan,” International Journal of Applied Earth Observation and Geoinformation, vol. 127, p. 103660, 2024.

SyntheWorld: A synthetic data extending OpenEarthMap.

Reseachers developed a remote sensing image synthesis system based on procedural modeling and generative AI models. A large-scale synthetic dataset, called SyntheWorld, was constructed for land cover mapping and building change detection to improve generalization when trained together with real data. The dataset is accessible here.

J. Song, H. Chen, and N. Yokoya, ”SyntheWorld: A large-scale synthetic dataset for land cover mapping and building change detection,” Proc. WACV, 2024.