Tackling Conservation and Business Challenges with Satellite Imagery


Building Resilient Cities

Leveraging Satellite Imagery data to analyze climate change, socio-economic trends and demographics and developing resilient cities

Smart Farming

Managing farms with Advanced Analytical infrastructure to optimize the human labor and improving quantity and quality of products

Fighting Deadly Diseases

Helping government and nonprofits fight deadly diseases by mapping out large urban areas with strategic and granular accuracy

GEOBOX-Based Sustainability Solutions


The AI-powered application we developed with Microsoft and Evergreen provides an integrated view of urban areas most impacted by urban heat island (UHI) and project future UHI levels.


We built a flood risk assessment model using satellite imagery, a computer vision model to classify roof structures, and other risk indicators derived from geospatial analytics.


We collaborated with Microsoft and the World Mosquito Program (WMP) to deliver an AI model that optimizes efforts to neutralize the disease carrying capacity of mosquitoes.


Leveraging the Perks of Spatial Analysis

Spatial analysis is the process of geographically modeling a problem or issue, deriving results by computer processing, and then examining and interpreting those model results.

The global geospatial analytics market size is expected to grow by USD 96.3 billion in 2025, at a CAGR of 12.9% during the forecast period of 5 years.

Businesses and nonprofits are utilizing satellite imagery, geospatial mapping, and remote sensing technologies to apply deep learning on datasets to create workflows and build solutions.

Naveen Gattu
COO, Co-founder
Proud Microsoft Gold Partners
“Over the last few years, we have been a niche ISV partner for Microsoft in developing cloud-first Data and AI solutions for Enterprises, Nonprofits, and Governments. Gramener achieving the Gold ISV Partnership provides us greater access to the Microsoft cloud and leverages products to provide value-driven data solutions to our clients.”


Gramener, as Microsoft’s World mosquito Program’s data science partner, is developing Machine Learning for an AI model to fight against mosquito-borne diseases.


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We are building sustainable solutions to build resilient cities. Our solution, AI for Resilient Cities – an AI-enabled data visualization tool, aims to help federal governments and municipalities across cities plan for and mitigate the impacts of climate change.


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This application leverages deep learning to process images captured from satellite, enriched with census data, and offers insights about rise in urbanization and poverty, and anomalies in census-measured factors like literacy, employment, and healthcare.

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Our deep learning solution can predict crop yield with high spatial resolution several months before harvest, using openly available satellite image source. It can help making informed planting decisions, identifying low-yield regions and more.


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Our spatial analytics capabilities are helping disaster relief organizations save millions of lives by predicitng disasters before hand.

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This webinar is curated for any business user, AI experts, Machine Learning experts, and Advanced Analysts who want to use Geospatial Analytics to build a plethora of innovative solutions.

Spatial analysis and satellite imagery are giving birth to innovative solutions that can solve public health issues and conservation challenges. Find out how!

Geospatial Artificial Intelligence is an emerging interdisciplinary concept that combines Spatial Analysis and advances in Artificial Intelligence and Machine Learning. Know more about it.

This article comprises 3 major use cases and spatial data science application which we developed to fight dengue with World Mosquito Program (WMP), Evergreen Canada and Microsoft AI for Earth.

Find out multiple use cases of spatial analysis technology in public health intervention.

Article on geospatial AI and it’s use cases

Find out what is crop yield and how can farmers enable infrastructures to predict it using machine learning algorithms channeling data from IoT devices, sensors and satellite imagery

Find out what devices are used to collect what data to promote advanced and digital agriculture strategies. Know what is smart farming and how to enable it.

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