Peer-Reviewed Research Foundation

Verifiable Science

Commercial products built on transparent, peer-reviewed research — transforming uncertainty into defensible intelligence for critical decisions.

4+
Publications
Peer-Reviewed
95%+
AUC Accuracy
Model Performance
99%
Wetland Mapping
Ecosystem Accuracy
GDE
Google Expert
Earth Engine
Explore Science
Core Methodology

From Academic Excellence to Market Leadership

Spatialyte's foundation combines deep academic expertise, proven market validation, and mastery of scalable technologies — creating distinct competitive advantages through three core pillars.

Verifiable Scientific Rigor

Our models aren't black boxes. They're built on methodologies rigorously tested and published in leading scientific journals with transparent validation.

Peer-Reviewed

Cloud-Native Scalability

Leveraging Google Earth Engine and High-Performance Computing, we process petabytes of geospatial data with unprecedented efficiency and scale.

Petabyte Scale

Explainable AI (XAI)

Clear, interpretable results that explain why a risk score exists. SHAP analysis transforms predictions into actionable, defensible insights.

SHAP Enabled
Technology Infrastructure

Our Technology Stack

Built on industry-leading platforms and cutting-edge methodologies that enable planetary-scale geospatial analysis with scientific precision.

GeoAI

Advanced machine learning algorithms specifically optimized for geospatial data analysis and pattern recognition

ML Optimized

Google Earth Engine

Planetary-scale geospatial analysis platform enabling petabyte-scale satellite data processing

GDE Certified

High-Performance Computing

Slurm-managed HPC clusters for massive parallel data processing and model training

Parallel Processing

XGBoost & LGBM

State-of-the-art gradient boosting frameworks for maximum predictive accuracy

95%+ AUC
Explainable AI (XAI)

Beyond Prediction: Understanding the 'Why'

A significant challenge with many AI solutions is their "black box" nature. Spatialyte directly addresses this through focus on Explainable AI (XAI).

SHAP Analysis Framework

For financial institutions reporting on climate risk under TCFD framework, or insurance companies justifying premiums, understanding specific factors driving risk assessments is critical.

By employing SHAP (Shapley Additive exPlanations), we transform model outputs from unverified numbers into actionable, defensible insights that stakeholders trust and regulators can audit.

Regulatory Compliance: TCFD-aligned explanations
Risk Factor Analysis: Elevation, rainfall, proximity impacts
Audit Trail: Defensible decision documentation

Example SHAP Output

Elevation Factor
+0.45
Rainfall Frequency
+0.32
River Proximity
+0.28
Drainage Density
+0.18
Final Risk Score 0.87
Publications Library

Built on Peer-Reviewed Research

Our commercial solutions are founded on transparent, scientifically validated methodologies published in leading academic journals.

A high-resolution national-scale flood susceptibility and exposure assessment for Pakistan

Waleed, M., et al.

International Journal of Disaster Risk Reduction (2024)

Featured

Provides the framework for national-scale risk intelligence, identifying that ~95 million people (47% of population) are exposed to high flood susceptibility in Pakistan. This methodology forms the basis for our government-scale risk assessment services.

95% AUC Accuracy
30m Resolution
National Coverage

Machine learning-based spatial-temporal assessment of wetlands

Waleed, M., Sajjad, M., et al.

Ecological Informatics (2023)

EcoTerra

Demonstrates our capability to achieve 99% accuracy in monitoring critical ecosystems using Random Forest machine learning. This research underpins our EcoTerra Metrics service for carbon credit verification and biodiversity monitoring.

99% Accuracy
Multi-decadal Analysis
Ecosystem Monitoring

Comparative assessment of machine learning models for flood susceptibility mapping

Waleed, M., et al.

Journal of Flood Risk Management (2024)

HydroRisk

Comprehensive evaluation of 14 different machine learning algorithms for flood prediction, establishing LGBM and XGBoost as optimal choices. This research enables our algorithm selection framework for custom flood susceptibility modeling.

14 ML Models Tested
SHAP Analysis
Algorithm Optimization

Assessing the impacts of urbanization on terrestrial carbon storage in Pakistan

Waleed, M., et al.

Environmental Impact Assessment Review (2023)

Climate

Quantifies a 1040% increase in urban areas and corresponding 5% decrease in carbon storage over 30 years. This temporal analysis methodology supports our ClimaScribe Analytics for urban heat assessment and carbon monitoring services.

30-Year Time Series
National Scale
Carbon Impact
World-Class Expertise

Founded on Expert Leadership

Google Developer Expert (GDE) in Earth Engine

Certified expertise in planetary-scale geospatial analysis, ensuring our solutions leverage the most advanced cloud-native technologies available for unprecedented scale and efficiency.

GDE
Google Certified
4+
Publications
Petabyte
Data Processing

Our founder's unique combination of academic rigor and technical mastery ensures that Spatialyte delivers not just data, but defensible, peer-reviewed intelligence for your most critical environmental risk decisions.

Ready to Get Started?

Experience Science-Backed Intelligence

Ready to leverage our peer-reviewed methodologies and transparent GeoAI solutions for your environmental risk assessment needs?

Peer-Reviewed
Scientific Validation
95%+ AUC
Model Accuracy
SHAP XAI
Explainable Results
TCFD Ready
Compliance Aligned