提出遥感长时序理解新基准与模型，解决地理演化追踪难题。
GeoChrono: Benchmarking and Rethinking Long-Term Temporal Understanding in Remote Sensing
- 构建四层认知评估体系，分解长期时空理解能力
- 主流多模态模型在长期记忆上表现远低于人类专家
- 设计轨迹编码器与渐进压缩器，提升演化建模效率
遥感为观测地球表面长期演变提供了独特视角，但要求模型不仅感知特定时刻的地表覆盖，还需追踪变化、记忆演化历史，并跨时空推理。现有研究缺乏对这些能力的系统评估。为此，我们提出ChronoBench，一个包含12个子任务和17,689个严格验证的问答对的多维基准，将任务分解为四个渐进的认知层级：地表覆盖感知、时间识别、长期记忆和时空推理。大量评估显示，主流多模态大模型（MLLMs）在长期记忆方面表现严重不足，成为主要瓶颈。受此启发，我们进一步提出GeoChrono，一种具备增强演化追踪、记忆与推理能力的MLLM。利用地理区域空间位置固定而语义随时间演变的物理先验，设计了用于构建逐位置时间轨迹的时序轨迹编码器（TempEnc），并引入粗到细令牌压缩器（C2FComp），自适应保留动态区域的同时压缩静态背景。为支持训练，还构建了包含10.4万样本的ChronoInstruct指令微调数据集。GeoChrono在ChronoBench上达到当前最优性能，优于领先商用MLLM超过20%；C2FComp使视觉令牌减少56%以上，同时保持94.6%的性能。代码与数据将开源。
原文摘要 · Abstract (English)
Remote sensing offers an unparalleled vantage point for observing the Earth's long-term surface evolution, yet it demands that a model not only perceive land cover at isolated moments, but also track changes, memorize evolution histories, and reason across time and space. However, existing studies lack a systematic evaluation that dissects these distinct competencies. To fill this gap, we introduce ChronoBench, a multidimensional benchmark that decomposes this task into four progressive cognitive levels (i.e., Land Cover Perception, Temporal Recognition, Long-Term Memory, and Spatio-Temporal Reasoning). The ChronoBench comprises 12 sub-tasks and 17,689 rigorously validated QA (Question-Answer) pairs. Extensive evaluations reveal that mainstream MLLMs fall drastically behind human experts, with Long-Term Memory emerging as the most critical bottleneck. Motivated by this finding, we further propose GeoChrono, an MLLM with enhanced capabilities for tracing, memorizing, and reasoning about long-term geographic evolution. Leveraging the physical prior that geographic parcels remain spatially fixed while their semantics evolve, we design a Temporal Trajectory Encoder~(TempEnc) that constructs per-location temporal trajectories for dedicated land cover evolution modeling, and we introduce a Coarse-to-Fine Token Compressor~(C2FComp) that adaptively preserves dynamic regions while compressing the static background. To support training, we also construct ChronoInstruct, a 104K-sample instruction-tuning dataset spanning all competency levels for training. GeoChrono achieves state-of-the-art performance on ChronoBench, surpassing the leading commercial MLLMs by over 20%, while C2FComp reduces visual tokens by over 56% while retaining GeoChrono's 94.6% performance. The code and data will be available at https://github.com/IntelliSensing/GeoChrono
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