Linden Climate Labs | Remote Sensing & Machine Learning for the Built Environment

Our Technology

Action-Conditioned Urban World Models

Moving beyond static image-to-image correlation. We formulate urban microclimates as dynamic state-transition systems using Joint Embedding Predictive Architectures (JEPA) to evaluate "what-if" counterfactuals.

01 / OBSERVATION

Multi-Scale Satellite Tokenization

Ingesting raw 10m multi-spectral optical reflectance (Copernicus Sentinel-2) and 2D morphological vectors (OpenStreetMap) into a patch-based Vision Transformer—preserving sharp parcel boundaries without heuristic downscaling.

02 / FORCING

Dynamic Atmospheric Conditioning

Coupling local physical form with exogenous macro-meteorological vectors from ERA5 reanalysis (solar flux, ambient temperature, humidity, wind vectors), enabling simulation under historical baselines and extreme future heat domes.

03 / SIMULATION

Latent Causal Prediction (JEPA)

Simulating physical infrastructure interventions directly within abstract representation space. This eliminates pixel-level noise, overcomes regression to the mean, and captures true non-linear vegetative cooling thresholds.

Commercial & Civic Impact

Engineered for Rapid Urban Decision-Making

Providing verified counterfactual intelligence to the industries shaping the built environment.

AEC

Architecture & Engineering

Simulate site design permutations in milliseconds during master planning. Win municipal RFPs with quantitative microclimate impact proof without running slow, expensive fluid dynamics simulations.

B2B

Material & Coating Vendors

Arm sales teams with verified before-and-after heat reduction certificates. Automate parcel lead scouting to identify commercial properties with maximum thermal cooling leverage.

GRID

Utilities & Municipalities

Pinpoint neighborhood distribution transformers at risk of thermal blowout during peak summer demand. Prioritize urban forestry and cool pavement capital under federal Justice40 mandates.

Peer-Reviewed • Sustainable Cities and Society

Proven Causal Deep Learning Architecture

Our foundational models build directly upon published causal research conducted in Detroit, Michigan. By coupling high-resolution remote sensing with NASA ECOSTRESS satellite thermal observations, our baseline framework demonstrated exceptional predictive fidelity:

0.95 ± 0.005

Cross-Validated R²

0.40 K

Mean Absolute Error

100%

Directional Accuracy

View Published Article on ScienceDirect (Elsevier)