SDG 11 · Southeast Asia

Science-Based
Urban Spatial Planning

Open geospatial platform powered by Big Earth Data, satellite imagery, and AI to help cities in Indonesia, Malaysia, and Vietnam grow sustainably.

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LULC Raster Spatial Analytics Projection Timeline

What is SciBUSP?

An integrated web platform that turns raw satellite data into actionable urban planning insights — from land-use classification to growth projection and zoning compliance.

Multi-Layer GIS
Raster & vector data management with cloud-optimized tiles
Satellite Imagery
Land-use classification from multi-temporal Earth observation data
AI Analytics
Machine learning & Markov chain models for urban growth prediction
Real-Time Monitoring
Track sprawl, compliance, and vulnerability as cities evolve

Analytical Modules

Five purpose-built modules guide you step-by-step — from data upload to publication-ready results.

01

Data Management

Upload raster (GeoTIFF) and vector (GeoJSON) layers, auto-convert to Cloud-Optimized GeoTIFF, and manage metadata in one place.

Foundation
02

Land Use Compliance

Overlay LULC rasters with RTRW zoning plans to identify areas that comply — or violate — official spatial regulations.

Regulation
03

Urban Sprawl Analysis

Compare two time-period rasters to quantify built-up area change, identify sprawl hotspots, and measure expansion rates.

Detection
04

Growth Scenario Modeling

Build what-if scenarios for urban expansion using customizable road proximity, constraint zones, and population density drivers.

Simulation
05

Markov Chain Projection

Multi-class land-change projection using Cellular Automata–Markov Chain with transition matrices, suitability maps, and optional accuracy validation (Kappa & OA).

Projection
06

Executive Dashboard

Bird's-eye view of all analyses, KPI summaries, recent activity, and quick-access links across all modules.

Overview

How It Works

From raw satellite data to planning insights in four steps.

1

Upload Data

Upload LULC rasters, zoning vectors, DEM, and other spatial layers via the data manager.

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2

Select & Inspect

Choose time-period layers, inspect class distributions, and label land-use categories.

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3

Run Analysis

Execute sprawl detection, growth simulation, compliance checks, or Markov projections.

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4

Explore Results

Interactive maps, KPIs, transition matrices, and exportable reports for decision-making.

SDG 11

Why SciBUSP Matters

Rapid urbanization across Southeast Asia demands evidence-based spatial planning. SciBUSP bridges the gap between satellite science and local governance by providing open, reproducible tools that any planning agency can deploy.

Monitor Vulnerable Regions

Track urban risks and exposure patterns with satellite data and change detection.

Optimize Allocation

Prioritize infrastructure investment with data-driven scenario modeling.

Mitigate Urban Sprawl

Detect expansion early and evaluate spatial regulation effectiveness.

Build Resilience

Strengthen climate adaptation using geospatial insights and AI forecasts.

Supported By

A multi-country collaboration led by BRIN under the ANSO-funded SciBUSP initiative.

BRIN — Indonesia CAS-RADI — China UTM — Malaysia VAST — Vietnam ANSO

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