This application discloses a slope unit extraction method, belonging to the field of geological
engineering technology. It includes: based on multi-source topographic data of the target area, performing physical constraint-based preprocessing and multi-scale
feature extraction to construct a multi-source fusion feature
tensor that integrates spectral features, elevation features, and multi-scale topographic location index features from
remote sensing images; secondly, inputting the feature
tensor into a TerrainNet
deep learning model to obtain an initial topographic feature line prediction map representing the probability distribution of ridgelines and valley lines. The model integrates a large
receptive field spatial attention mechanism, a bidirectional feature decoding mechanism, and a multi-objective joint
loss function; finally, based on the prediction map, output
processing is performed, generating several slope units representing planar polygons by identifying hanging endpoints, obtaining local directional vectors, and performing topological extension and closure
processing. This method realizes the prediction of topographic feature lines and topological
connectivity repair, improving the
automation and structural accuracy of slope unit extraction.