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)

使用光学高分辨率图像和 OpenStreetMap（OSM）数据进行土地覆盖变化检测，引入目标引导的 Transformer（ObjFormer）架构，结合目标基于图像分析（OBIA）技术和先进的视觉 Transformer 架构，实现了直接检测土地覆盖变化，并提出了一种新的半监督语义变化检测任务，能高效地完成此任务。