  Optical high-resolution imagery and OSM data are two important data sources
of change detection (CD). Previous related studies focus on utilizing the
information in OSM data to aid the CD on optical high-resolution images. This
paper pioneers the direct detection of land-cover changes utilizing paired OSM
data and optical imagery, thereby expanding the scope of CD tasks. To this end,
we propose an object-guided Transformer (ObjFormer) by naturally combining the
object-based image analysis (OBIA) technique with the advanced vision
Transformer architecture. This combination can significantly reduce the
computational overhead in the self-attention module without adding extra
parameters or layers. ObjFormer has a hierarchical pseudo-siamese encoder
consisting of object-guided self-attention modules that extracts multi-level
heterogeneous features from OSM data and optical images; a decoder consisting
of object-guided cross-attention modules can recover land-cover changes from
the extracted heterogeneous features. Beyond basic binary change detection,
this paper raises a new semi-supervised semantic change detection task that
does not require any manually annotated land-cover labels 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 negative samples, contributing to the great performance
improvement in this task. A large-scale benchmark dataset called OpenMapCD
containing 1,287 samples covering 40 regions on six continents is constructed
to conduct detailed experiments. The results show the effectiveness of our
methods in this new kind of CD task. Additionally, case studies in Japanese
cities demonstrate the framework's generalizability and practical potential.
The OpenMapCD and source code are available in
https://github.com/ChenHongruixuan/ObjFormer
