GeoChrono

GeoChrono is a multimodal large language model (MLLM) tailored for long-term remote sensing interpretation. Given an image sequence spanning multiple years and a text prompt, GeoChrono generates a response through spatio-temporal reasoning — tracing, memorizing, and reasoning about the long-term evolution of geographic entities.

GeoChrono is introduced in the paper GeoChrono: Benchmarking and Rethinking Long-Term Temporal Understanding in Remote Sensing (accepted at ACM MM 2026 🔥), together with the ChronoBench benchmark and the ChronoInstruct instruction-tuning dataset.

• 📦 Code: https://github.com/IntelliSensing/GeoChrono
• 📊 Data (ChronoBench & ChronoInstruct): https://huggingface.co/datasets/Davidup1/GeoChrono-Data

Model Architecture

GeoChrono comprises four core components: i) a Visual Encoder that extracts per-frame visual features, ii) a Vision-Language Projector that maps visual representations into the language embedding space, iii) a Temporal Trajectory Encoder (TempEnc) that models per-location temporal evolution to strengthen the perception of land-cover change history, and iv) a Large Language Model (LLM) that performs multimodal reasoning and generates the textual response.

TempEnc leverages the physical prior that each geographic parcel remains spatially fixed while its semantics evolve: it decouples the spatio-temporal feature volume into per-location temporal trajectories via three stages — Spatial Context Aggregation (a lightweight 3×3 depthwise convolution), Hybrid Temporal Attention (dual-stream bidirectional + causal self-attention along the temporal axis), and Semantic Focusing (text-guided cross-attention that suppresses task-irrelevant temporal information).

The companion Coarse-to-Fine Token Compressor (C2FComp) leverages prompt text embeddings to assess the task relevance of each spatial region, selectively preserving full-resolution fine tokens for salient areas while condensing the static background into compact coarse representations — reducing visual tokens by over 56% while retaining 94.6% of the full model's performance. C2FComp is available in the code repository.

Training

GeoChrono is built upon Qwen3-VL-4B-Instruct. During training, the Vision Encoder and Vision-Language Projector are frozen; TempEnc is randomly initialized and fully fine-tuned, while the LLM backbone is tuned with LoRA (r=32, α=64, on q_proj/k_proj/v_proj/o_proj). TempEnc adopts a single-layer attention architecture. The model is trained for one epoch on ChronoInstruct (104K samples) using 4 NVIDIA H100 80GB GPUs. All images are fed at a fixed 1024×1024 resolution.

Repository Contents

File	Description
adapter_model.safetensors / adapter_config.json	LoRA adapter for the Qwen3-VL-4B-Instruct LLM backbone
ottc_weights.pt / ottc_config.json	TempEnc weights and configuration (ottc is the internal codename of TempEnc)
tokenizer.json, preprocessor_config.json, ...	Tokenizer and processor files (identical to the base model)

Usage

GeoChrono requires the custom TempEnc injection code from the GitHub repository — it cannot be loaded with vanilla transformers + peft alone:

git clone https://github.com/IntelliSensing/GeoChrono.git

Follow the inference / evaluation instructions in the repository README: the base model is Qwen/Qwen3-VL-4B-Instruct, on top of which the LoRA adapter is applied and the TempEnc module (ottc_weights.pt) is injected into the model forward pass.

Results on ChronoBench

GeoChrono achieves state-of-the-art performance on ChronoBench, surpassing the leading commercial MLLMs by over 20% and reaching human-level accuracy on several sub-tasks:

Method	LCP	TR	LTM	STR	OA
Human	97.04	89.78	91.73	95.56	92.28
Gemini-3-Flash	65.51	61.38	47.52	59.89	57.48
GPT-5.4	43.53	67.57	39.21	50.42	56.29
Qwen3-VL-32B	43.76	57.88	24.06	47.23	46.73
DVLChat-4B	47.74	34.21	22.91	40.59	44.07
GeoChrono (ours)	88.65	83.03	68.10	72.92	78.34

(LCP: Land Cover Perception, TR: Temporal Recognition, LTM: Long-Term Memory, STR: Spatio-Temporal Reasoning, OA: Overall Accuracy. All values in %.)

GeoChrono also generalizes to unseen distributions: under zero-shot settings it reaches 72.9 / 42.1 / 35.7 on the three DVL-Bench sub-tasks and 59.2 on CDVQA, outperforming all remote sensing domain baselines.

License

This model is released under the Apache License 2.0. The base model Qwen3-VL-4B-Instruct is subject to its own license.

Citation

If you find GeoChrono useful, please cite our paper GeoChrono: Benchmarking and Rethinking Long-Term Temporal Understanding in Remote Sensing. The BibTeX entry will be provided here once the arXiv preprint is released — stay tuned.

Contact

For questions or feedback, please open a discussion or contact liyujie2003@bupt.edu.cn.