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Meticulous Geospatial Annotations
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Geospatial Annotation in Machine Learning

Image annotation is the process of marking and outlining objects and entities in an image and attributing various tags to classify it in a manner readable to machines. Geospatial annotation encompasses qualifying images captured from the aerial mode and those from the satellites into AI & Machine compatible datasets. This leads to a built-in real-time dataset to assess and provide critical actionable data to businesses. Mapping large farms, construction sites, mines, real estate developments, disaster recovery situations & geographical features are a few commonly annotated Geospatial imagery. Geospatial annotation provides an invaluable source of input data for machine learning tools & is becoming increasingly vital in the context of algorithms that allow for efficient access and retrieval of images from large Geospatial datasets.

Types of Geospatial Annotation

POI Tagging image - NextWealth computer vision service application

POI Tagging

A Point of Interest (POI) is a specific geographic entity such as an office, a garage, a public plaza, a yard, a tourist attraction, a school etc. Points of interest forms the base for most of the data supporting location-based applications. POI Tagging provides a dataset that is a quick, easy and accurate way to support a host of applications like digital mapping, remote surveillance, area-level land zoning, validation of private databases etc. POI Tagging also creates a unique database for governments and other industries with exact geometric coordinates to aid in various developmental projects. POI tagging is a combination of spatial and non-spatial data(capturing images & the collation of attributes of a related asset like name & address) and allows businesses to create dynamic systems that reflect the usage of various services by the people.

Damage Area Annotation - NextWealth computer vision services

Damage Area Annotation

Damage Area Annotation provides organizations with an automatic system for the assessment of damage from high-resolution imagery requiring little or no manual supervision. Numerous natural & human-induced disasters threaten vulnerable areas of the world quite often. Damage Area Annotation deploys Advanced computer vision approaches to manage post-event rescue, reconstruction & insurance plans efficiently. DAA assessment systems rely on optical data, which are processed to detect & assess the level of damage. According to the available data, these approaches either use only post-event images or both pre and post-event images. In the field of auto insurance, images of an auto accident, including the conditions, scene, and repairs needed are extracted & analyzed to provide insights. They also aid and speed up the inspection process, benefiting both insurers and the insured. Damage Area Annotation datasets fed to the AI Algorithms also scrutinize vehicle imagery to help assess damage post accidents.

Footprint Annotation image - NextWealth image annotation service

FootPrint Annotation

Footprint maps provide the outline of a building drawn along the exterior walls, with a description of the exact size, shape, and location of its plinth. Buildings have come to be known as the basic ‘unit’ of our civilization & an essential parameter to measure various indicators. Footprint annotation provides the accurate data needed to develop building footprints of the required location by an artificial intelligence algorithm capable enough to identify and extract building footprint data and deliver it in a usable format. The number of buildings within a given perimeter, risk identification from surrounding natural/industrial elements, and complete locational accuracy by verifying a building’s exact location through rooftop geofencing are a few applications powered by Footprint annotation. This ever-growing domain finds application in Insurance, Construction, Real Estate, Navigation, Social & Economic Organisations, Defense, Urban planning, Rural development etc. 

Applications of Geospatial annotation

Mapmaking

Geospatial annotation can be used to create rich maps by adding information about the features of location such as roads, buildings, and rivers, to a base map.

Geographic Information System(GIS) Analysis

Geospatial annotation can be used to create data layers that are used in GIS analysis. This allows users to analyze and visualize spatial data to understand patterns and relationships. Spatial relationships may display topography, such as agricultural fields and streams, display land usage patterns such as location of parks, housing complexes etc.

Disaster Response

Geospatial annotation can be used to map areas affected by natural disasters, such as earthquakes, hurricanes, tsunamis and floods, in order to aid in rescue and reconstruction efforts.

Environmental Monitoring

Geospatial annotation can be used to track the movement of wildlife, monitor changes in land use or land cover, and monitor the health of both terrestrial & marine ecosystems.

Tourism Sector

Geospatial annotation can be used to create context-rich maps that show the locations of tourist attractions, hotels, and other points of interest, which can be useful for tourists and travel agents.

Real Estate

Geospatial annotation can be used to create maps that show the locations of properties which can be used by realtors and buyers/renters.

Case Studies

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