Land classification on Sentinel 2 data using a simple sklearn cluster algorithm or deep learning CNN Read A brief introduction to satellite image classification with neural networks While image-level classification assigns a single label to an entire image, semantic segmentation assigns a label to each individual pixel in an image, resulting in a highly detailed and accurate representation of the land cover types in an image. It is important to note that image-level classification should not be confused with pixel-level classification, also known as semantic segmentation. This can be accomplished using a combination of feature extraction and machine learning algorithms to accurately identify the different land cover types. In these cases, image-level classification becomes more complex and involves assigning multiple labels to a single image. However, in some cases, a single image might contain multiple different land cover types, such as a forest with a river running through it, or a city with both residential and commercial areas. The process of assigning labels to an image is known as image-level classification.
The UC merced dataset is a well known classification dataset.Ĭlassification is a fundamental task in remote sensing data analysis, where the goal is to assign a semantic label to each image, such as 'urban', 'forest', 'agricultural land', etc. □ Conversation between Robin and Derek Ding, the co-founder of the Orbuculum platform.This integration of cutting-edge technology with socially impactful missions could position Orbuculum as an instrumental platform at the intersection of scientific research and sustainable development. By providing access to vital data and insightful analytics, Orbuculum promises to act as a potent resource in the ongoing battle against some of the most urgent global concerns. It is poised to serve as an invaluable conduit for public welfare initiatives, especially those striving to mitigate climate change. Orbuculum's potential extends far beyond the reinvention of the GIS/EO research industry. This enables automatic remuneration for the creators each time their models are deployed, fostering an efficient and rewarding ecosystem. Standing distinctively apart from conventional marketplaces, Orbuculum pioneers a transformative approach by transmuting these models into smart contracts. Orbuculum is an innovative and rapidly evolving platform designed with the specific intent to empower GIS and Earth Observation (EO) researchers by offering a unique avenue for monetizing their machine learning models. This repository is proudly sponsored by Orboculum. How to use this repository: use Command + F (Mac) or CTRL + F (Windows) to search this page, and note that searching the raw markdown file can be more effective It covers a range of architectures, models, and algorithms suited for key tasks like classification, segmentation, and object detection. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing. Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes.