Geospatial Object Detection via Auto-Generated Deep Learning Models
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Solution Overview
Problem
The field of geospatial image analysis is limited by the need for highly trained and expensive image analysts, and existing computer vision systems struggle to reliably identify objects in geospatial imagery due to technological and logical barriers, such as sensitivity to lighting and object orientation.
Innovation Solution
A system using auto-generated deep learning models, specifically a deep learning model training software module and an image analysis software module, which processes orthorectified geospatial images to identify and locate objects of interest through convolutional neural networks, applying image modification steps and a multi-scale sliding window algorithm to account for orientation and scale differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional image analysis methods are used, then object identification can be performed, but highly trained and expensive image analysts are required
Solution Approach 1:
The patent replaces the mechanical system of human image analysts with an automated computer-based deep learning system. The system uses convolutional neural networks and auto-generated deep learning models to perform object identification in geospatial imagery, eliminating the need for highly trained human analysts while maintaining or improving identification accuracy.
Solution Approach 2:
The system employs self-training capabilities where the deep learning model automatically generates training data from unlabeled geospatial images. The model performs self-supervised learning by identifying objects in unlabeled images and using these identifications to improve its own performance, reducing the need for manual annotation and expert intervention.
2Extent of automation
If computer vision systems are used to identify objects, then automation is improved, but reliability is reduced due to sensitivity to lighting and object orientation
Solution Approach 1:
The system performs preliminary actions by pre-processing geospatial images to normalize lighting conditions and orientations before object identification. The deep learning model is trained on diverse datasets that include variations in lighting, orientation, and scale, enabling it to reliably identify objects regardless of these variations in production images.
Solution Approach 2:
The system changes parameters by applying image transformation techniques such as rotation, scaling, and lighting normalization to input images. The deep learning model is trained to recognize objects across multiple parameter variations, making the identification process reliable regardless of the original image's lighting conditions or object orientations.
3Measurement precision
If manual image analysis is used, then accuracy can be maintained, but productivity is reduced due to the need for specialized analysts
Solution Approach 1:
The patent replaces manual image analysis with an automated deep learning system that achieves comparable or superior accuracy while dramatically increasing productivity. The system can process large volumes of geospatial imagery rapidly, identifying objects such as buildings, vehicles, and infrastructure elements without the limitations of human analyst availability.
Solution Approach 2:
The deep learning model is designed to identify multiple types of objects across different categories (e.g., buildings, vehicles, infrastructure, natural features) within a single unified system. This multi-functional capability allows the system to maintain high accuracy across diverse object types while processing images at speeds far exceeding manual analysis capabilities.
4Adaptability or versatility
If existing computer vision systems are used, then some object identification is possible, but adaptability is limited to controlled environments
Solution Approach 1:
The system employs dynamic adaptability through continuous learning and model updates. The deep learning model can be retrained and fine-tuned on new types of geospatial imagery and object categories, allowing it to adapt to different environments and applications. The system dynamically adjusts to new object types, lighting conditions, and geographic regions through iterative training processes.
Solution Approach 2:
The system performs preliminary actions by pre-training on diverse geospatial datasets that encompass various environments, terrains, and object types. This pre-training establishes a robust foundation that enables the model to adapt to new environments with minimal additional training, reducing the complexity of deploying the system across different applications.
Data Source
AI summary
A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.