地理空間智能與信息系統(tǒng)：適用于遙感、街景等地理大數(shù)據(jù)的多模態(tài)基礎(chǔ)模型；地理空間推理；基于地理空間智能的信息共享系統(tǒng)與可信數(shù)據(jù)空間等。

能源地理與氣候韌性：利用地理空間智能、計(jì)量經(jīng)濟(jì)學(xué)、能源系統(tǒng)建模等方法，探索“能源-氣候-社會(huì)”復(fù)雜聯(lián)系及其時(shí)空異質(zhì)性，為因地制宜制定政策提供可解釋的參考依據(jù)，以加速碳中和進(jìn)程并提升“基礎(chǔ)設(shè)施-人類”耦合系統(tǒng)的韌性。

代表性論文

• 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).

• 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.

• 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)

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

• 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)

其它論文

• 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.

• 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)

• 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.

• 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.

• 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.

• 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).

• 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.

• 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).

• 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)

• 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)

• 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)

• 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.

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