Publications

Selected Publications

• Zhecheng Wang, Michael Wara, Arun Majumdar, and Ram Rajagopal (2023). Local and Utility-Wide Cost Allocations for a More Equitable Wildfire-Resilient Distribution Grid. Nature Energy. (Featured as cover). (link) (Stanford News) (The Hill)

• Zhecheng Wang, Marie-Louise Arlt, Chad Zanocco, Arun Majumdar, and Ram Rajagopal (2022). DeepSolar++: Understanding Residential Solar Adoption Trajectories with Computer Vision and Technology Diffusion Models. Joule. (link) (Stanford News)

• Jiafan Yu*, Zhecheng Wang*, Arun Majumdar, and Ram Rajagopal (2018). DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. Joule. (Featured as cover). (* Equal contribution) (link) (website) (Stanford News) (MIT Technology Review)

• Zhecheng Wang, Arun Majumdar, and Ram Rajagopal (2023). Geospatial Mapping of Distribution Grid with Machine Learning and Publicly-Accessible Multi-Modal Data. Nature Communications. (link)

• Zhecheng Wang, Rajanie Prabha*, Tianyuan Huang*, Jiajun Wu, and Ram Rajagopal (2024). SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing. AAAI Conference on Artificial Intelligence. (* Equal contribution) (link)

Other Publications

• Tianyuan Huang, Chad Zanocco, Zhecheng Wang, Jackelyn Hwang, and Ram Rajagopal (2025). Neighborhood Built Environment Disparities are Amplified During Extreme Weather Recovery. Accepted in principle by Nature.

• Tony Liu, Chad Zanocco, Zhecheng Wang, Tianyuan Huang, June Flora, and Ram Rajagopal (2025). Large Language Model Enabled Knowledge Discovery of Building-Level Electrification Using Permit Data. Energy and Buildings. (link)

• Rajanie Prabha, Zhecheng Wang, Chad Zanocco, June Flora, and Ram Rajagopal (2025). DeepSolar-3M: An AI-Enabled Solar PV Database Documenting 3 Million Systems Across the US. ICLR Tackling Climate Change with Machine Learning Workshop. (Best Paper Award) (link)

• Moritz Wussow, Chad Zanocco, Zhecheng Wang, Rajanie Prabha, June Flora, Dirk Neumann, Arun Majumdar, and Ram Rajagopal (2024). Exploring the Potential of Non-Residential Solar to Tackle Energy Injustice. Nature Energy. (link) (Stanford News) (Tech Xplore)

• Tianyuan Huang, Timothy Dai, Zhecheng Wang, Hesu Yoon, Hao Sheng, Andrew Ng, Ram Rajagopal, and Jackelyn Hwang (2022). Detecting Neighborhood Gentrification at Scale via Street-level Visual Data. IEEE International Conference on Big Data. (link)

• Kevin Mayer, Benjamin Rausch, Marie-Louise Arlt, Gunther Gust, Zhecheng Wang, Dirk Neumann, and Ram Rajagopal (2022). 3D-PV-Locator: Large-Scale Detection of Rooftop-Mounted Photovoltaic Systems in 3D. Applied Energy. (link)

• Tianyuan Huang*, Zhecheng Wang*, Hao Sheng*, Andrew Ng, and Ram Rajagopal (2021). M3G: Learning Urban Neighborhood Representation from Multi-Modal Multi-Graph. ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data. (* equal contribution). (link)

• Mingxiang Chen, Qichang Chen, Lei Gao, Yilin Chen, and Zhecheng Wang (2021). Predicting Geographic Information with Neural Cellular Automata. AAAI AI for Urban Mobility Workshop. (link)

• Kevin Mayer, Zhecheng Wang, Marie-Louise Arlt, Dirk Neumann, and Ram Rajagopal (2020). DeepSolar for Germany: A Deep Learning Framework for PV System Mapping from Aerial Imagery. International Conference on Smart Energy Systems and Technologies (SEST). (link)

• Zhecheng Wang*, Haoyuan Li*, and Ram Rajagopal (2020). Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding. AAAI Conference on Artificial Intelligence. (* Equal contribution) (link)

• Qinghu Tang*, Zhecheng Wang*, Arun Majumdar, and Ram Rajagopal (2019). Fine-Grained Distribution Grid Mapping Using Street View Imagery. NeurIPS Tackling Climate Change with Machine Learning Workshop. (* Equal contribution) (link)

• Zhengcheng Wang*, Zhecheng Wang*, Arun Majumdar, and Ram Rajagopal (2019). Identify Solar Panels in Low Resolution Satellite Imagery with Siamese Architecture and Cross-Correlation. NeurIPS Tackling Climate Change with Machine Learning Workshop. (* Equal contribution) (link)

• Sharon Zhou, Jeremy Irvin, Zhecheng Wang, Eva Zhang, Jabs Aljubran, Will Deadrick, Ram Rajagopal, and Andrew Ng (2019). DeepWind: Weakly Supervised Localization of Wind Turbines in Satellite Imagery NeurIPS Tackling Climate Change with Machine Learning Workshop. (link)

• Neel Guha, Zhecheng Wang, and Arun Majumdar (2018). Machine Learning for AC Optimal Power Flow. ICML Climate Change Workshop. (Best Paper Award Honorable Mention) (link)