# JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering

arXiv: https://arxiv.org/abs/2606.31745 · HTML: https://mlsanity.com/paper/2606.31745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 60 of its 133 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 30 of 60)

- [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)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [Learning to Describe Differences Between Pairs of Similar Images](https://mlsanity.com/paper/1808.10584/) (2018)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)
- [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://mlsanity.com/paper/2410.06234/) (2024)
- [Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2108.07002/) (2021)
- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)
- [Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2309.01429/) (2023)
- [RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent](https://mlsanity.com/paper/2406.07089/) (2024)
- [CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection](https://mlsanity.com/paper/2406.04207/) (2024)
- [A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection](https://mlsanity.com/paper/2312.01163/) (2023)
- [Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents](https://mlsanity.com/paper/2509.23141/) (2025)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2202.12361/) (2022)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)
- [Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model](https://mlsanity.com/paper/2406.17998/) (2024)
- [TINYCD: A (Not So) Deep Learning Model For Change Detection](https://mlsanity.com/paper/2207.13159/) (2022)
- [DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response](https://mlsanity.com/paper/2505.21089/) (2025)
- [Segment Any Change](https://mlsanity.com/paper/2402.01188/) (2024)
- [HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2404.09178/) (2024)
- [Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2404.09179/) (2024)
- [Change Detection Meets Visual Question Answering](https://mlsanity.com/paper/2112.06343/) (2021)
- [FCCDN: Feature Constraint Network for VHR Image Change Detection](https://mlsanity.com/paper/2105.10860/) (2021)

## Abstract (arXiv)

Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why. To bridge this semantic gap, we introduce JL1-CC&QA, a multi-task benchmark that extends the JL1-CD dataset with two complementary annotation layers: change captioning (CC) and change question answering (QA). Built upon 5,000 bi-temporal image pairs acquired by the Jilin-1 satellite at 0.5-0.75m ground sample distance, the benchmark comprises: (i) JL1-CC, providing 17,021 quality-verified captions that describe diverse land-cover transformations; and (ii) JL1-QA, offering 20,060 question-answer pairs across eight question types, enabling fine-grained, interactive interrogation of surface changes. All annotations are produced via a three-stage pipeline consisting of multi-modal large language model (LLM) generation, vision-grounded LLM judging, and human expert verification. We hope that JL1-CC&QA, as a benchmark unifying binary change masks, change captions, and change-oriented QA over the same image set, will serve as a valuable resource for the community to advance multi-task change understanding in remote sensing. The dataset is available at https://github.com/circleLZY/JL1-CD.