1) The document discusses developing a deep learning tool for map tiles using pix2pix. It presents the workflow for using pix2pix on map tiles, including downloading data, preparing training data, running the model, and applying the trained model.
2) Case studies are shown applying the tool to generate semantic maps from Landsat imagery and DEM data, including a paddy rice map and vegetation map. A third case converts an old black and white topographic map to a land use map.
3) While the results show potential, further improvement and accuracy evaluation is still needed. Overall map tiles are concluded to be a valuable source of geospatial data for deep learning applications.
1) The document discusses developing a deep learning tool for map tiles using pix2pix. It presents the workflow for using pix2pix on map tiles, including downloading data, preparing training data, running the model, and applying the trained model.
2) Case studies are shown applying the tool to generate semantic maps from Landsat imagery and DEM data, including a paddy rice map and vegetation map. A third case converts an old black and white topographic map to a land use map.
3) While the results show potential, further improvement and accuracy evaluation is still needed. Overall map tiles are concluded to be a valuable source of geospatial data for deep learning applications.