{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:50Z","timestamp":1758672890899,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Accurate canal network mapping is essential for water management, including irrigation planning and infrastructure maintenance. State-of-the-art semantic segmentation models for infrastructure mapping, such as roads, rely on large, well-annotated remote sensing datasets. However, incomplete or inadequate ground truth can hinder these learning approaches. Many infrastructure networks have graph-level properties such as reachability to a source (like canals) or connectivity (roads) that can be leveraged to improve these existing ground truth. This paper develops a novel iterative framework IGraSS, combining a semantic segmentation module\u2014incorporating RGB and additional modalities (NDWI, DEM)\u2014with a graph-based ground-truth refinement module. The segmentation module processes satellite imagery patches, while the refinement module operates on the entire data viewing the infrastructure network as a graph. Experiments show that IGraSS reduces unreachable canal segments from ~18% to ~3%, and training with refined ground truth significantly improves canal identification. IGraSS serves as a robust framework for both refining noisy ground truth and mapping canal networks from remote sensing imagery. We also demonstrate the effectiveness and generalizability of IGraSS using road networks as an example, applying a different graph-theoretic constraint to complete road networks.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1076","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9683-9691","source":"Crossref","is-referenced-by-count":0,"title":["IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation"],"prefix":"10.24963","author":[{"given":"Oishee Bintey","family":"Hoque","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Virginia"}]},{"given":"Abhijin","family":"Adiga","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia"}]},{"given":"Aniruddha","family":"Adiga","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia"}]},{"given":"Siddharth","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Earth System Science Cente, University of Alabama in Huntsville"}]},{"given":"Madhav V.","family":"Marathe","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia"},{"name":"Department of Computer Science, University of Virginia"}]},{"given":"S.S.","family":"Ravi","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia"}]},{"given":"Kirti","family":"Rajagopalan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Systems Engineering, Washington State University"}]},{"given":"Amanda","family":"Wilson","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia"}]},{"given":"Samarth","family":"Swarup","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:03Z","timestamp":1758627363000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1076"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1076","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}