Geospatial Science
research, modelling & spatial analytics
GLOBEIR's Geospatial Science practice supports research institutions, universities, government agencies, and enterprises with rigorous spatial analysis, predictive modelling, remote sensing research, and publication-grade geospatial science.
What we deliver
Remote Sensing Research
Multi-spectral analysis, hyperspectral processing, SAR interferometry, time-series analysis, and change detection using Landsat, Sentinel, MODIS, and CartoSAT datasets.
GeoAI & Machine Learning
Deep learning for land-cover classification, object detection in satellite imagery, predictive spatial modelling, and AI-driven feature extraction.
Spatial Statistics & Modelling
Spatial autocorrelation (Moran's I, Geary's C), regression (GWR, OLS), kriging, geostatistics, and spatial econometrics for research-grade analysis.
Environmental & Climate Studies
Land surface temperature, vegetation indices (NDVI, EVI), urban heat island analysis, glacier monitoring, deforestation, and climate change indicators.
Geospatial Data Pipelines
Reproducible research workflows using Python, R, Google Earth Engine, and cloud GIS — designed for scientific rigour and peer review.
Publication & Reporting Support
Journal-ready maps, figures, and statistical outputs. Method documentation, code repositories, and supplementary materials for peer-reviewed publications.
Real-world applications
Trusted across industries to solve location-based challenges with precision and scale.
Powered by modern technology
Google Earth Engine
Python (GeoPandas, rasterio, xarray)
R (sp, sf, raster, spatstat)
QGIS / GRASS GIS
ENVI / ERDAS Imagine
PyTorch / TensorFlow for GeoAI
SNAP (Sentinel Toolbox)
Jupyter / RMarkdown Notebooks
Frequently asked questions
What is Geospatial Science?
Geospatial Science is the interdisciplinary study of Earth's features and phenomena using spatial data — combining geography, remote sensing, GIS, statistics, and computer science to understand patterns, processes, and changes on the planet.
Do you support academic research?
Yes. GLOBEIR partners with universities, research institutes (IITs, IIScs, ICRISAT, ISRO-affiliated bodies), and PhD students for rigorous spatial analysis, custom remote sensing studies, and methodology development for publication.
Can you process satellite data at scale?
Yes. We leverage Google Earth Engine, AWS Open Data Registry, and Microsoft Planetary Computer for cloud-scale processing of Landsat, Sentinel, MODIS, and commercial high-resolution imagery — terabytes to petabytes.
Do you provide GeoAI / machine learning services?
Yes. We build deep learning models for land-cover classification, object detection (buildings, roads, vehicles, vessels), change detection, and predictive spatial modelling using PyTorch, TensorFlow, and specialized libraries like TorchGeo and Raster Vision.
What is the difference from regular GIS?
Geospatial Science emphasizes scientific rigour, reproducible methodology, statistical validation, and contribution to knowledge — typically for research, policy, or publication. Regular GIS focuses on operational use of spatial data for day-to-day decisions.
Can you co-author research publications?
In suitable collaborations, yes. We have contributed methodology and analysis to peer-reviewed papers in journals covering remote sensing, environmental science, urban studies, and geospatial AI.
Advance your geospatial research
From a single analysis to a multi-year research program — let's collaborate on rigorous geospatial science.