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

arXiv: https://arxiv.org/abs/2310.02674 · HTML: https://mlsanity.com/paper/2310.02674/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 77 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification](https://mlsanity.com/paper/2106.02034/) (2021)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels](https://mlsanity.com/paper/2203.03884/) (2022)
- [How Much Position Information Do Convolutional Neural Networks Encode?](https://mlsanity.com/paper/2001.08248/) (2020)
- [Learning from Complementary Labels](https://mlsanity.com/paper/1705.07541/) (2017)
- [OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2210.10732/) (2022)
- [Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection](https://mlsanity.com/paper/2201.06030/) (2022)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2212.05245/) (2022)
- [Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange](https://mlsanity.com/paper/2310.00689/) (2023)
- [A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2102.08041/) (2021)
- [Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning](https://mlsanity.com/paper/2210.00941/) (2022)
- [BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning](https://mlsanity.com/paper/2304.07076/) (2023)

## Built on by (most cited first, 8 of 8)

- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [DynamicEarth: How Far are We from Open-Vocabulary Change Detection?](https://mlsanity.com/paper/2501.12931/) (2025)
- [OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2501.10891/) (2025)
- [Pushing Trade-Off Boundaries: Compact yet Effective Remote Sensing Change Detection](https://mlsanity.com/paper/2506.21109/) (2025)
- [LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment](https://mlsanity.com/paper/2411.19758/) (2024)
- [Operational Change Detection for Geographical Information: Overview and Challenges](https://mlsanity.com/paper/2503.14109/) (2025)
- [Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation](https://mlsanity.com/paper/2504.14306/) (2025)
- [Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation](https://mlsanity.com/paper/2608.26489/) (2026)

## Abstract (arXiv)

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