arXiv:2608. 13344v1 Announce Type: new Abstract: Long-horizon Earth observation reasoning requires models to organize multi-stage geographic evolution, localize spatial changes, detect temporal anomalies, and infer future from extended image sequences.
By Yupan Ding, Jing Xiao, Zhenyuan Zhang, Chaofeng Chen, Liang Liao, Gui-Song Xia, Mi Wang
arXiv:2608. 10494v1 Announce Type: new Abstract: Earth observation (EO) agents construct scientifically valid tool workflows and ground their conclusions in current geospatial evidence.
By Xin Xiao, Jiang Zhong, Junnan Zhu, Yingchao Feng, Peijin Wang, Yidan Zhang, Kaiwen Wei
arXiv:2606. 11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs).
By Enhan Zhao, Wei Wu, Yuanrui Zhang, Xueliang Zhao, Di He
arXiv:2607. 29527v1 Announce Type: cross Abstract: A defining problem of the Anthropocene is to model the physical Earth and human societies as one coupled system, yet no learned representation spans their observational breadth.
By Carlos Rodriguez-Pardo, Massimo Tavoni
LT-Mem introduces a volatility‑aware memory evolution framework for lifelong scene understanding, combining spatially aligned instance‑level 3D perception with temporal reasoning. It uses a multi‑session SLAM backbone, a reasoning layer that scores evidence and selects memory actions, and a Tri‑Memory structure (Live, Delta, Meta) to preserve current states and event histories. The accompanying LT‑VQA dataset provides multi‑session recordings, persistent identity annotations, and temporal QA pairs, and experiments show LT‑Mem outperforms baselines while using far fewer tokens.
arXiv:2607. 12177v1 Announce Type: new Abstract: The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models.
By Shelley Cazares