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Spatial Representations for Artificial Intelligence - a Python library toolkit for geospatial machine learning focused on creating embeddings for downstream tasks
The main features of kraina-ai/srai are: Python Geospatial AI.
Projects with overlapping indexed features include: benmack/eo-box — A toolbox for processing earth observation data with Python. c2g-dev/city2graph — Transform geospatial relations into graphs for Graph Neural Networks and network analysis. diegovicen/som-tsp — This project is a neural network route optimizer and unsupervised learning tool designed to solve the traveling…
Transform geospatial relations into graphs for Graph Neural Networks and network analysis
This project is a neural network route optimizer and unsupervised learning tool designed to solve the traveling salesman problem. It functions as a self-organizing map solver that calculates near-optimal paths through a set of coordinates to determine the shortest possible tour. The system utilizes a Kohonen map implementation to organize high-dimensional data into a lower-dimensional representation. It employs competitive learning and topology preservation to approximate solutions for combinatorial optimization problems. The solver covers route optimization analysis and heuristic pathfindin
