AI Data for Geospatial Applications

High-quality training data to help geospatial models identify and extract real-life objects and events from geospatial or aerial imagery using computer vision, artificial intelligence, and machine learning

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Geospatial Annotation

Annotating & Labeling Data to Incorporate AI into Your Geospatial Models

Enhance the performance and precision of your geospatial machine learning model with accurately annotated and labeled Earth’s observatory image libraries. Cogito’s data annotation & labeling capabilities allow you to build, extend, and deploy AI into customized geospatial models/applications.

Geospatial Data Annotation Techniques

Instance / Semantic Segmentation

Instance / Semantic Segmentation

By segmenting and annotating geospatial images at the pixel level, Cogito’s computer vision experts are able to develop algorithms that can detect and track objects.

Polygon Annotation

Polygon Annotation

Expert annotators plot points on each vertex of the target object. Polygon annotation allows all of the object’s exact edges to be annotated, regardless of shape.

Lidar Annotation

Lidar Annotation

Cogito banks upon its own experts to build high-quality training data for ML models by annotating images captured using multisensor cameras with 360-degree visibility.

Key Point Annotation

Key Point Annotation

The key point annotation helps develop AI for identifying small objects and shape variations by marking the locations and minor geospatial characters.

Geospatial Data Use Cases

In addition to placing location and timing in traditional data types, it is used to draw maps, graphs, statistics, and cartograms to paint a more complete picture of the geophysical events and site evaluation.

Field & Forest Analysis

Field & Forest Analysis

Artificial intelligence can help protect flora and fauna in forests by analyzing drone-shot aerial photos of forests & agricultural fields. Annotating forest actions in images can train machine learning models to detect and manage forests accurately.

Construction & Mining

Construction & Mining

AI and machine learning can enhance efficiency in several areas, including project planning and construction monitoring. Artificial intelligence is increasingly used at construction and mining sites to enhance worker safety and reduce operational costs.

Urban Area Management

Urban Area Management

Civil engineers and architects are using geospatial machine learning to plan and manage urban areas. Geospatial data annotations can help cities train their AI, big data analytics, and machine learning models for better development.

Disaster Management

Disaster Management

Enabling AI to predict natural disasters to assist rescue teams in managing, monitoring, and setting out immediate and workable disaster recovery plans through situational awareness, decision support, and the development of hazard maps.

Logistics & Supply Management

Logistics & Supply Management

The logistics management and supply management experts can use AI-enabled automation and predictive analytics to visualize the routing and logistics decisions on a map and determine the best routes and schedules.

Aerospace & Defence

Aerospace & Defence

Geospatial agencies and operators can identify potential threats by analyzing and visualizing data in real time. Users of all technical abilities can access geospatial information and location intelligence based on modern, up-to-date databases.

Your Trusted Geospatial AI Data Partner

Geospatial partners are a part of our global network. Our team can deliver application-specific training data to help you reach new heights in geospatial practice, regardless of the use case. As part of our AI training data practice, we adhere to a broad ethical agenda, which includes GDPR, CCPA, Fair Pay pledge, diversity, and inclusion.

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