Advancing geospatial intelligence through automation, precision, and AI-driven analysis
The InferenceVision project contributes to geospatial science by integrating automated object detection with precise coordinate computation. These contributions enhance the scalability, accuracy, and usability of geospatial datasets for research, planning, and operational decision-making.
By leveraging deep learning object detection models such as YOLO via the Ultralytics library, InferenceVision automatically identifies and localizes objects in high-resolution satellite and aerial imagery. This reduces human workload, accelerates data processing pipelines, and allows for real-time geospatial intelligence generation.
The framework converts pixel-based detections into WGS 84 geographic coordinates, ensuring centimeter-to-meter level accuracy. By integrating spatial transformations, bounding box centroids, and polygon corner referencing, the system produces reliable geolocation data suitable for urban planning, environmental monitoring, and disaster response applications.
InferenceVision is designed to support multiple application domains, including precision agriculture, smart city management, flood monitoring, and emergency logistics. Its modular architecture allows easy integration of new object classes, coordinate systems, and AI models tailored to specific datasets and operational needs.
To improve interpretability and assist technical users, InferenceVision integrates fine-tuned language models for domain-specific guidance:
InferenceVision-Pythia-1B provides detailed explanations and step-by-step guidance for object detection pipelines, coordinate calculations, and geospatial data interpretation. It is trained on domain-specific documentation to ensure high fidelity technical answers.
InferenceVision-GPTNeo-1.3B excels in structured Q&A, offering clarifications on detection workflows, polygon mapping, and coordinate transformations. It has been fine-tuned using a curated set of technical and geospatial datasets.
Dive deeper into how InferenceVision integrates language models for geospatial Q&A, including usage examples, model fine-tuning steps, and interactive queries.
Open Notebook