The Change You Want To Talk About: Enabling spatial and narrative Change Detection in Earth Observation with a single VLM architecture

Bi-temporal change detection (CD) of Earth Observation images is fragmented into disconnected spatial and textual prediction tasks. While recent Vision Language Models (VLM) explore open-vocabulary and query-driven challenges, their semantic flexibility is often constrained by decoupled, post-hoc matching pipelines. Besides, they often struggle to capture fine-grained temporal dynamics and remain restricted to isolated tasks, such as dense prediction, captioning, or grounding. We propose LOSCAR, a multi-task VLM capable of addressing diverse CD tasks under a unique paradigm, through specific prompting. By integrating a spatio-temporal architecture with a spatial decoder that translates multimodal features into dense image masks, our model jointly generates natural language descriptions and explicit spatial outputs under a continuous open-vocabulary regime. After multi-dataset training, our experiments show that LOSCAR achieves state-of-the-art results in change captioning while delivering accurate spatial localization and robust reasoning on complex semantic change trajectories.