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)
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)