Desktop Context Overview — A refined 3D GIS planning view placing Higashiyama Context House within Kyoto’s low-rise urban fabric, with solar exposure, garden buffer, street setback, material transition, site metrics, and Higashiyama Ward map context.

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Environmental Architecture

Higashiyama Context House

Multi-device 3D GIS planning tool for a Kyoto residence, focused on site context, sunlight, movement, and neighborhood scale.

Year

2024

Duration

4 months

Location

Kyoto, Kyoto Prefecture, Japan

Surfaces

Stack

Architecture

GCPPostgreSQL with PostGIS

Mapping

ArcGIS ProArcGIS Maps SDK for JavaScriptArcGIS SceneViewArcGIS MapViewArcGIS Scene LayersGSI Japan geospatial dataVector tile basemapsGeoJSON planning layers

Code

ReactNext.jsTypeScriptSwiftUIWebGLglTF/GLBPython geospatial processingResponsive GIS interface components

GeminiVertex AICloud Vision AIOpenAI Responses APIVegetation classification workflowsShadow and solar summary agentsContext-aware design note generationSpatial planning report summarization

Conceptual application work, created with the listed platforms and APIs and tested across the indicated surfaces.

Details

Completed in July 2024 for a private builder in Kyoto as a delivered geospatial architecture and planning application

Presented Higashiyama Context House as part of a living urban system rather than as an isolated architectural object

Combined 3D GIS, 2D planning views, environmental data, architectural visualization, and lightweight spatial analysis

Modeled garden buffers, canal-edge microclimate, pedestrian access, street setbacks, solar exposure, shadow footprint, material transitions, and low-rise residential context

Supported desktop, iPad, and iPhone review workflows with shared project data and device-specific layouts

Included English and Japanese interface views for local Kyoto review and international presentation workflows

Used transparent architectural rendering to expose interiors, stair circulation, terraces, planted decks, and relationships between floors

Applied AI-assisted interpretation for vegetation classification, environmental summaries, design callouts, site documentation, and planning-report narratives