Remote sensing for environmentally responsive urban built environment: A review of tools, methods and gaps

Abstract

Urban-scale environmental performance evaluations are essential for designing cities that effectively respond to climate change and rapid urbanization. Remote Sensing (RS) technologies provide high-resolution, multi-scale, and temporal assessments across multiple interlinked environmental criteria. Despite its growing adoption in urban sustainability, a comprehensive review of RS's role in multi-criteria decision-making is still lacking. This review analyzes 124 research articles to explore RS applications in spatio-temporal analysis, impact evaluation, mitigation strategy assessment, and predictive modeling across five interconnected environmental criteria: urban air quality, urban heat, outdoor thermal comfort, building energy consumption, and solar potential. RS facilitates the integration of morphological, thermal, and meteorological data, enabling the evaluation of urban interdependence, such as the influence of urban form on air pollution dispersion, heat retention, and energy demand. Machine learning and AI-enhanced models improve air quality predictions, urban heat mitigation strategies, energy forecasting, and solar potential assessments. UAVs, LiDAR, and nanosatellite technologies further enhance real-time urban climate monitoring at finer spatial scales, supporting dynamic planning interventions. Despite challenges in data resolution, temporal coverage, and real-time monitoring, advancements in AI-driven downscaling, digital twins, and nano satellite networks continue to expand RS capabilities. By facilitating multi-criteria decision-making, RS empowers urban designers and policymakers to develop climate-adaptive, energy-efficient, and resilient cities, offering actionable insights for sustainable design and planning.