{"type":"ScholarlyArticle","title":"DeepShade: Enable Shade Simulation by Text-conditioned Image Generation","authors":[{"name":"Longchao Da","slug":"longchao-da","given":"Longchao","family":"Da","affiliation":""},{"name":"Xiangrui Liu","slug":"xiangrui-liu","given":"Xiangrui","family":"Liu","affiliation":""},{"name":"Mithun Shivakoti","slug":"mithun-shivakoti","given":"Mithun","family":"Shivakoti","affiliation":""},{"name":"Thirulogasankar Pranav Kutralingam","slug":"thirulogasankar-pranav-kutralingam","given":"Thirulogasankar Pranav","family":"Kutralingam","affiliation":""},{"name":"Yezhou Yang","slug":"yezhou-yang","given":"Yezhou","family":"Yang","affiliation":""},{"name":"Hua Wei","slug":"hua-wei","given":"Hua","family":"Wei","affiliation":""}],"abstract":"Heatwaves pose a significant threat to public health, especially as global warming intensifies. However, current routing systems (e.g., online maps) fail to incorporate shade information due to the difficulty of estimating shades directly from noisy satellite imagery and the limited availability of training data for generative models. In this paper, we address these challenges through two main contributions. First, we build an extensive dataset covering diverse longitude-latitude regions, varying levels of building density, and different urban layouts. Leveraging Blender-based 3D simulations alongside building outlines, we capture building shadows under various solar zenith angles throughout the year and at different times of day. These simulated shadows are aligned with satellite images, providing a rich resource for learning shade patterns. Second, we propose the DeepShade, a diffusion-based model designed to learn and synthesize shade variations over time. It emphasizes the nuance of edge features by jointly considering RGB with the Canny edge layer, and incorporates contrastive learning to capture the temporal change rules of shade. Then, by conditioning on textual descriptions of known conditions (e.g., time of day, solar angles), our framework provides improved performance in generating shade images. We demonstrate the utility of our approach by using our shade predictions to calculate shade ratios for real-world route planning in Tempe, Arizona.  We believe this work will benefit society by providing a reference for urban planning in extreme heat weather and its potential practical applications in the environment.","doi":"10.24963/ijcai.2025/1068","url":"https://ijcai.org/proceedings/2025/1068","pdf":"https://ijcai.org/proceedings/2025/1068.pdf","markdown":"https://ijcai.org/proceedings/2025/1068.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1068","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI and Social Good","track":"AI and Social Good","number":1068,"pages":{"first":9610,"last":9618},"keywords":["Humans and AI: General","Data Mining: General","Multidisciplinary Topics and Applications: General","Computer Vision: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}