Advanced machine learning system trained on 12,000+ human satisfaction surveys that predicts occupant satisfaction with window views. Analyzes segmentation and depth information to generate precise view scores at directional, locational, room, unit, and building levels for comprehensive design optimization and property evaluation.
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Sophisticated analysis engine that evaluates visual privacy using neighboring building facades, urban context, street trees, and window positioning. Assesses exposure levels and privacy concerns while maintaining balance between natural light access, desirable views, and occupant security in dense environments.
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Comprehensive dataset containing 12,000+ annotated window view images with human satisfaction ratings, segmentation maps, and depth information. Serves as the foundation for machine learning model training and enables continuous improvement of view assessment accuracy and methodology validation.
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Transform property listings with automated view scoring and valuation insights. Integrates with MLS platforms, Zillow, and major listing services to help agents highlight premium views, justify pricing strategies, and differentiate properties through data-driven view quality metrics that resonate with buyers.
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Real-time CAD plugin for Rhino3D, AutoCAD, and other design software that provides instant feedback on view quality, privacy levels, and occupant satisfaction. Enables architects to simulate building orientations, window placements, and urban contexts before construction to maximize development value.
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Dynamic pricing and booking optimization system for hotels that leverages view quality scores for revenue management. Evaluates landmark visibility, water views, and nature access to help hospitality businesses optimize room rates, enhance guest satisfaction, and improve revenue per available room.
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Jaeha Kim
Project Lead Developer
Timur Dogan
Research Lead
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Email
jk2894@cornell.edu