Harris Census Block Group - Building Instance Population

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Authors: Jikun Liu, Akhil Anil Rajput
Cited by: 0

Abstract

This dataset contains building-level records for Harris County where each building instance is linked to a Census Block Group (GEOID) and assigned an estimated population label derived from demographic ratios and occupancy assumptions. It combines geometric/location attributes (building polygons or centroids), structural/context features (building type, estimated units, parcel-informed classification), and CBG-level demographic context (ppu_*, occupancy_rate) to create ML-ready supervision targets. The core table for modeling is ml_dataset/labels.csv, which ties each building_uid to image crop/mask paths and a numeric estimated_population target. Companion outputs include building-level estimate files (harris_county_building_population_estimates.csv, harris_county_buildings_with_population.geojson) and a CBG-level reference/validation table (harris_county_cbg_validation.csv) keyed by GEOID. Conceptually, it is a spatially joined, feature-engineered population estimation dataset where many buildings map to the same CBG, enabling both geospatial analysis and supervised learning.

Fields of Study & Subjects

Automated Road and Building Extraction Impact of Light on Environment and Health Census and Population Estimation

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 March 2026
Language
EN
Document type
Dataset