This dataset contains labeled aerial images of air fighters, bombers, armored personnel carriers, tanks and soldiers captured by reconnaissance drones during the russo-Ukrainian War, aimed at supporting the development of machine learning models for military object detection. The images simulate real-world conditions with diverse altitudes, angles, and lighting, providing a robust foundation for applications in automated surveillance and situational awareness. Ideal for defense and security contexts, this dataset enables precise detection and classification of military objects, aiding in real-time decision-making and enhancing overall reconnaissance capabilities.

Satellite Images Of Vehicles And People

## Overview

Satellite Images Of Vehicles And People is a dataset for object detection tasks - it contains Military Objects annotations for 1,157 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

RVLTS Experimental (non Satellite Imagery)

## Overview

RVLTS Experimental (non Satellite Imagery) is a dataset for object detection tasks - it contains Military Vehicle Detection RyaB annotations for 394 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Russian Military Vehicles (10 classes) annotated for object detection.

Dataset generously provided by:

Tuomo Hiippala Digital Geography Lab Department of Geosciences and Geography University of Helsinki, Finland E-mail: tuomo.hiippala@iki.fi

Here are a few use cases for this project:

1. Military Intelligence: Armed forces can use this model to analyze satellite and other aerial surveillance images for identifying and classifying different airborne machines, improving strategic planning and defense mechanisms.

2. Air Traffic Control: The system can be integrated with airport or aviation infrastructure to identify potential threats such as unauthorized drones in flight paths or traditional airspace, enhancing safety and security.

3. Research and Development: Aircraft manufacturers and defense scientists can use the model to identify the type and nature of airborne objects in test ranges, assisting in data collection, testing, and design of new models.

4. Unidentified Flying Object (UFO) Study: Aerospace agencies or astronomers can use it to filter out identifiable objects like aircraft, drones, and missiles in sky imagery, focusing on truly unidentified objects.

5. Public Safety: Government agencies can make use of the model for public safety by monitoring illegal or dangerous drone use, for instance, smuggling contraband across borders or drones venturing into restricted areas.

Here are a few use cases for this project:

1. Smart Geometry Learning Applications: The model can be incorporated into educational software and mobile applications to assist students, particularly youngsters, in learning different geometric shapes. Through image-based practical examples, learning can become more engaging and lively.

2. Advanced Cartography Software: The model could be used in cartography to detect different geometric shapes in aerial or satellite imagery for mapping purposes. For example, identifying particular building structures, parks or field designs based on the shapes could prove to be useful.

3. Traffic and Road Analysis: The "Target Detection" system could be used in transportation for identifying road sign shapes from traffic monitoring CCTV footage or autonomous vehicle visual input data. This can enhance traffic management and autonomous driving systems.

4. Military and Defense Strategy: In military and defense, the model can be employed for surveillance operations to identify specific structures or symbols from aerial or satellite imaging. The shape identification could assist in the location of enemy bases, strategic points, or signify certain movements.

5. Urban Planning and Development: Urban planners can leverage this model to analyze cityscape images for planning and developmental purposes. It can detect different shapes and learn about the space management and designing aspects of different structures.

Here are a few use cases for this project:

1. Urban Traffic Monitoring and Management: Satellite1 can be used to analyze satellite images of cities and estimate the number and types of vehicles on roads, providing valuable information to optimize traffic signal timing, identify traffic congestion, and suggest alternate routes for drivers.

2. Parking Space Management: The model can efficiently detect the types and quantity of vehicles in parking lots, helping in optimizing the allocation of spaces, estimating parking capacity requirements, and providing real-time information for drivers seeking parking spots.

3. Military and Border Surveillance: Satellite1 can be utilized to detect the presence of military vehicles, such as tanks and armored vehicles, along borders, aiding security forces in assessing security risks and responding to potential threats and incursions in a timely manner.

4. Disaster Response and Damage Assessment: In the aftermath of natural disasters or accidents, Satellite1 can quickly evaluate satellite imagery to detect blocked roads, stranded or damaged vehicles, and other obstacles that might impede relief efforts or require rapid assistance.

5. Infrastructure and Transportation Planning: By identifying the types and densities of vehicles in satellite images, urban planners and transportation authorities can gain insights into traffic patterns, usage of existing transportation infrastructure, and the potential need for additional infrastructure or public transit options.