What are the AI Applications for Satellite Imagery in Machine Learning?
AI applications for satellite imagery is expanded into the multiple levels for machine learning training to extract the useful information for developing the AI models for same fields. AI models are developed through machine learning or deep learning use in space monitoring through satellite images needs the training data for detecting the various objects from such altitude for field mapping or urban planning.
AI Applications for Satellite Imagery
Actually, in artificial satellites or manmade satellites, AI is used in two ways — One-level” applications and Multi-level” applications. So we need to discuss both the applications of AI for satellite imagery. And to clarify both level applications, it’s worth to mention that from technical perspective, these applications require complex machine learning pipeline for various types of projects.
“One-step” Satellite Data Applications
The first applications of satellite data is making the various objects detectable to computer vision. The objects like buildings, road segments, and urban area boundaries is important for municipalities, government agencies, rescue teams, military, and other civil agencies. And such things can easily monitored from space satellites through images captured from high-resolution cameras.
In AI developments, while object detection task has been enabled by a number of integrated ready-to-use pipelines using convolutional neural nets there are some issues with satellite imagery which make this task more challenging.
Detecting the Objects Looking Small
Actually, the first objects in satellite imagery are often very small, while input images are enormous and also there’s a relative scarcity of training data. Thus, the methods mentioned above are not optimized to detect small objects in large images, and often perform poorly when applied to Earth observation data.
Moreover, the second example, AI applications is satellite images is change detection, in which numerous applications, such as crop, land use, urban infrastructure like road segments or environmental (deforestation, water reserves) and humanitarian crisis monitoring.
The main motive of change detection algorithm is to create a map, in which changed areas are separated from unchanged ones. Two types of change detection can be further defined: A binary change detection and multi-class change detection, when each transition type can be explicitly identified.
“Multi-level” Satellite Data Applications
As discussed above, in multi-level applications, information, extracted from satellite imagery (using “one-level” methods described above) is only a line of features in more complex ML system. As a lot of thing are going on in this field I’d provide short description of various interesting use cases and potential verticals interested in such products.
From counting the cars at parking lots, crop yield and geo-sensing in agricultural fields, satellite imagery can provide the valuable inputs for machine learning and AI models. The automated algorithms can accurately detect various things visible on the earth from the space.
Gathering Data for Geo-sensing & Soil Monitoring
However, from the other side, it’s obvious that yield-related task cannot be solved only by vegetation indices, thus more features should be included, such as weather and soil-related data. And if we augment these features with prices and sentiment analysis, it’s possible to create a model beating official forecasts in terms of timing and accuracy.
Apart from that there are multiple other applications of AI in satellite imagery like ship count and sizes, aircraft count in airports, urban areas, water, roads, etc. for Aerial view monitoring and data analysis.
The satellite images are used for urban management or planning and also for smart city development using the satellite imagery datasets containing the useful information of annotated objects. And to create such the satellite images data for supervised machine learning image annotation services is available with the data annotation companies involved in image, text and video annotations.
Anolytics provides the image annotation services to annotate the satellite images for machine learning and AI projects. It can provide the satellite imagery datasets created through various image annotation techniques like bounding box annotation, semantic segmentation annotation and polygon annotation for different types of objects detection for different types of AI models like drones etc.
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