ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer

TL;DR

This paper introduces ObjFormer, a novel object-guided Transformer model that directly detects land-cover changes using paired OSM data and high-resolution optical imagery, enabling semi-supervised semantic change detection without manual labels.

Contribution

The paper presents a new Transformer-based framework combining OBIA with vision Transformer for direct land-cover change detection from paired data, and proposes a semi-supervised semantic change detection task with a large-scale benchmark dataset.

Findings

Effective in direct land-cover change detection from paired data

Enables semi-supervised semantic change detection without manual labels

Demonstrates generalizability across diverse geographic regions

Abstract

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