{"type":"ScholarlyArticle","title":"MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models","authors":[{"name":"Piyush Tiwary","slug":"piyush-tiwary","given":"Piyush","family":"Tiwary","affiliation":""},{"name":"Utkarsh Ahuja","slug":"utkarsh-ahuja","given":"Utkarsh","family":"Ahuja","affiliation":""},{"name":"Depanshu Sani","slug":"depanshu-sani","given":"Depanshu","family":"Sani","affiliation":""},{"name":"Aishwarya Jayagopal","slug":"aishwarya-jayagopal","given":"Aishwarya","family":"Jayagopal","affiliation":""},{"name":"Sagar Gubbi","slug":"sagar-gubbi","given":"Sagar","family":"Gubbi","affiliation":""},{"name":"Subhashini Venugopalan","slug":"subhashini-venugopalan","given":"Subhashini","family":"Venugopalan","affiliation":""},{"name":"Alok Talekar","slug":"alok-talekar","given":"Alok","family":"Talekar","affiliation":""},{"name":"Vaibhav Rajan","slug":"vaibhav-rajan","given":"Vaibhav","family":"Rajan","affiliation":""}],"abstract":"Agricultural landscape segmentation in the Global South is challenging as it is characterized by fragmented plots, high intra-class variance, and a scarcity of labeled training data. Recent advances in segmentation have been made by Multimodal Large Language Models (MLLMs). However, current approaches encounter critical context length bottlenecks and a domain alignment gap in understanding satellite features. We address these limitations through MAgSeg, a novel, decoder-free MLLM segmentation approach. MAgSeg is an architecturally efficient approach that enables standard MLLMs to perform segmentation of complex smallholder agricultural landscapes from high-resolution satellite imagery, without requiring auxiliary vision decoders. We introduce a novel instruction tuning data format designed to enable scalable fine-tuning and post-training on high resolution satellite imagery, which enables MAgSeg to learn from the global context of the image while generating text tokens for only a patch within the image. Extensive evaluations on datasets spanning three countries in the Global South demonstrate that MAgSeg significantly outperforms state-of-the-art MLLM baselines, offering a scalable solution to map smallholder agricultural environments.","doi":"10.24963/ijcai.2026/728","url":"https://ijcai.org/proceedings/2026/728","pdf":"https://ijcai.org/proceedings/2026/0728.pdf","markdown":"https://ijcai.org/proceedings/2026/728.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/728","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":728,"pages":{"first":6546,"last":6554},"keywords":["Domain-specific AI4Tech: AI4Agriculture"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}