10.18130/V3/AYLB4SRiya GhateRiyaGhatehttps://ror.org/0153tk833Jingran Chenhttps://ror.org/0153tk833Aparna KishoreAparnaKishorehttps://ror.org/0153tk833Madhav V MaratheMadhavV Marathehttps://ror.org/0153tk833University of Virginia Dataverse2026Computer and Information ScienceEngineeringData center, Remote sensing, Spatial detection, Power estimationKishore, AparnaAparnaKishoreUniversity of Virginia2026-05-152026-05-152026-09-25646412467100326642126859141739315279629822798317648text/x-python-scripttext/x-python-scripttext/x-python-scripttext/x-python-scripttext/tab-separated-valuestext/x-python-scripttext/x-python-scripttext/x-python-scripttext/x-python-scripttext/x-python-scriptapplication/x-ipynb+json2.1CC0 1.0Data centers are among the fastest-growing loads on the U.S. electrical grid. Virginia, the world's largest data center market, is an ideal testbed for analyzing energy and infrastructure impacts. However, researchers and policymakers lack open, consistent facility-level data for effective regional planning and impact analysis. Existing datasets are often inaccurate, outdated, or too expensive for open use. We present VDC-SENSE (Virginia Data Center Satellite-derived ENergy and Spatial Estimates), an open multi-attribute dataset of 382 data center facilities in Virginia, constructed from remote sensing and public geospatial data using computer vision, machine learning, and statistical models. Each record provides 18 facility attributes: spatial footprint and geometry, construction year, IT whitespace and built-out power capacity, three distinct 24-hour power profiles, and contextual proximity to electrical, transportation, water, and residential infrastructure. Spatial detections achieve Pearson r > 0.99 against two independent facility registries; construction year estimates achieve 86.4% within-one-bin accuracy. The dataset can be regenerated as new imagery becomes available and supports energy systems modeling, grid planning, and policy analysis.