SAM_geospatial_training Computer Vision Project

SAM training

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Classes (20)
Expressway-Service-area
Expressway-toll-station
airplane airport
baseballfield
basketballcourt
bridge chimney
dam
golffield
groundtrackfield
harbor
overpass
ship
stadium
storagetank
tenniscourt
trainstation
vehicle
windmill
Description

Here are a few use cases for this project:

  1. Urban Development Monitoring: Government agencies or urban planners could use this model to monitor and analyze urban development over time. They can track the creation of new structures like bridges, dams, stadiums, and train stations and monitor changes in existing structures or features.

  2. Disaster Response Planning: SAM_geospatial_training can assist in disaster response strategies by identifying essential landmarks (airports, harbors, windmills, bridges, etc.) to assess their condition post-disaster or to plan evacuation routes.

  3. Environmental Conservation: Conservation organizations could utilize this model to monitor changes in land use like new constructions or destruction of fields, which could impact local ecosystems, weather patterns, or wildlife habitats.

  4. Military Surveillance: Defense departments could use this tool for surveillance purposes, monitoring enemy territory for changes in infrastructure such as airports, bridges, or harbors, or to locate military assets like tanks and airplanes.

  5. Traffic and congestion analysis: Transportation departments or smart city initiatives could deploy this model to monitor and manage traffic flow on expressways and at toll stations, identify congested routes, and plan infrastructure improvements to mitigate traffic issues.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            sam_geospatial_training_dataset,
                            title = { SAM_geospatial_training Dataset },
                            type = { Open Source Dataset },
                            author = { SAM training },
                            howpublished = { \url{ https://universe.roboflow.com/sam-training/sam_geospatial_training } },
                            url = { https://universe.roboflow.com/sam-training/sam_geospatial_training },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-12-24 },
                            }
                        
                    

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