Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer

Optical high-resolution imagery and OpenStreetMap (OSM) data are two important data sources for land-cover change detection. Previous studies in these two data sources focus on utilizing the information in OSM data to aid the change detection on multi-temporal optical high-resolution images. This paper pioneers the direct detection of land-cover changes utilizing paired OSM data and optical imagery, thereby broadening the horizons of change detection tasks to encompass more dynamic earth observations. To this end, we propose an object-guided Transformer (ObjFormer) architecture by naturally combining the prevalent object-based image analysis (OBIA) technique with the advanced vision Transformer architecture. The introduction of OBIA can significantly reduce the computational overhead and memory burden in the self-attention module. Specifically, the proposed ObjFormer has a hierarchical pseudo-siamese encoder consisting of object-guided self-attention modules that extract representative features of different levels from OSM data and optical images; a decoder consisting of object-guided cross-attention modules can progressively recover the land-cover changes from the extracted heterogeneous features. In addition to the basic supervised binary change detection task, this paper raises a new semi-supervised semantic change detection task that does not require any manually annotated land-cover labels of optical images to train semantic change detectors. Two lightweight semantic decoders are added to ObjFormer to accomplish this task efficiently. A converse cross-entropy loss is designed to fully utilize the negative samples, thereby contributing to the great performance improvement in this task. The first large-scale benchmark dataset containing 1,287 map-image pairs (1024$\times$ 1024 pixels for each sample) covering 40 regions on six continents ...(see the manuscript for the full abstract)

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