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)
Publications by Topics
For distributed renewable energy:
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)
Jeremy Irvin, Yuan Fang, Eric Matsumoto, JK Hunt, Zhecheng Wang, Hao Sheng, Jenny Yang, Kyle Story, Rose Rustowicz, Andrew Ng, and Ram Rajagopal. Scalable Wind Turbine Detection and Height Estimation using Deep Learning on Satellite Imagery. Under revision.
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)
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)
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)
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 (2018). DeepWind: Weakly Supervised Localization of Wind Turbines in Satellite Imagery NeurIPS Tackling Climate Change with Machine Learning Workshop. (link)
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. (* Equal contribution) (link)
For electrical grid infrastructure:
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. (link)
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)
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)
Neel Guha, Zhecheng Wang, and Arun Majumdar (2019). Machine Learning for AC Optimal Power Flow. ICML Climate Change Workshop. (Honorable Mention) (link)
For urban environments:
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)
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). M3G: Learning Urban Neighborhood Representation from Multi-Modal Multi-Graph. AAAI AI for Urban Mobility Workshop. (* equal contribution) (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)
For machine learning foundation models:
- Zhecheng Wang, Rajanie Prabha*, Tianyuan Huang*, Jiajun Wu, and Ram Rajagopal (2024). SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing. To appear in AAAI Conference on Artificial Intelligence. (* Equal contribution) (link)