Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns

2026-04-14

AI Summary

This study addresses the limitations of existing approaches to urban climate inverse problems, which often yield non-diverse solutions and produce only averaged vegetation configurations. To overcome this, we propose a hybrid inverse modeling framework that integrates a physics-driven forward thermal model with a conditional diffusion generative model. Our method is the first to explicitly incorporate solution diversity into vegetation spatial optimization guided by urban cooling objectives. By leveraging a controllable generation mechanism, it infers multiple physically plausible vegetation layouts—unseen in the training data—from prescribed land surface temperature changes. Experiments demonstrate that the framework efficiently generates feasible designs with high thermal regulation accuracy and spatial diversity, even under data-scarce conditions, thereby significantly enhancing the flexibility and scientific rigor of urban green infrastructure planning.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Diffusion Models for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Urban areas are increasingly vulnerable to thermal extremes driven by rapid urbanization and climate change. Traditionally, thermal extremes have been monitored using Earth-observing satellites and numerical modeling frameworks. For example, land surface temperature derived from Landsat or Sentinel imagery is commonly used to characterize surface heating patterns. These approaches operate as forward models, translating radiative observations or modeled boundary conditions into estimates of surface thermal states. While forward models can predict land surface temperature from vegetation and urban form, the inverse problem of determining spatial vegetation configurations that achieve a desired regional temperature shift remains largely unexplored. This task is inherently underdetermined, as multiple spatial vegetation patterns can yield similar aggregated temperature responses. Conventional regression and deterministic neural networks fail to capture this ambiguity and often produce averaged solutions, particularly under data-scarce conditions. We propose a conflated inverse modeling framework that combines a predictive forward model with a diffusion-based generative inverse model to produce diverse, physically plausible image-based vegetation patterns conditioned on specific temperature goals. Our framework maintains control over thermal outcomes while enabling diverse spatial vegetation configurations, even when such combinations are absent from training data. Altogether, this work introduces a controllable inverse modeling approach for urban climate adaptation that accounts for the inherent diversity of the problem. Code is available at the GitHub repository.

Problem

• inverse modeling
• urban vegetation
• land surface temperature
• thermal extremes
• spatial configuration

Innovation

• inverse modeling
• diffusion generative model
• urban vegetation design
• land surface temperature control
• multi-solution ambiguity