Parse Semantics from Geometry: A Remote Sensing Benchmark for Multi-modal Semantic Segmentation

Geometric information in the normalized digital surface models (nDSM) is highly correlated with the semantic class of the land cover. Exploiting two modalities (RGB and nDSM (height)) jointly has a great potential to improve the segmentation performance. Towards a fair and comprehensive analysis of existing methods, in this paper, we introduce a remote sensing benchmark for multi-modal semantic segmentation based on RGB-Height (RGB-H) data. The introduced RSMSS dataset contains 9340 tiles collected from three different cities, including the Oklahoma, Washington, D.C., and Philadelphia. Each RGB image tile has a corresponding nDSM map provided with the resolution of 1024x1024. We split all the image tiles into three subsets: the training set with 5,137 tiles, the validation set with 1,059 tiles and the test set with 3,144 tiles. All the image pixels are annotated with six different land cover types, including 1. ground; 2. low-vegetation; 3. building; 4. water; 5. road; 6. tree     «

Geometric information in the normalized digital surface models (nDSM) is highly correlated with the semantic class of the land cover. Exploiting two modalities (RGB and nDSM (height)) jointly has a great potential to improve the segmentation performance. Towards a fair and comprehensive analysis of existing methods, in this paper, we introduce a remote sensing benchmark for multi-modal semantic segmentation based on RGB-Height (RGB-H) data. The introduced RSMSS dataset contains 9340 tiles collec...     »