Technology Category
Computer Vision: Diffusion Models for VisionIntelligent Robots: Multimodal Perception & Sensor FusionMachine Learning: Time-Series/Data Streams
A framework for river connectivity classification using temporal image processing and attention based neural networks
🤖 AI Summary
To address the low efficiency and poor generalizability of manual interpretation of infrared camera imagery for dynamic river connectivity monitoring, this paper proposes an end-to-end framework for automatic stream wet/dry state classification. Methodologically, it introduces a novel architecture integrating temporal image enhancement with a vision Transformer; proposes foliage-aware luma variance reduction—a preprocessing technique that mitigates canopy-induced illumination variability; employs diffusion models for scene-adaptive generative data augmentation; and incorporates a seven-stage image quality filtering pipeline, bottom–center cropping, and multi-frame temporal aggregation. Evaluated on a cross-site, field-collected, expert-annotated dataset from Connecticut (2018–2020), the framework achieves a substantial accuracy improvement—from 75% to 90%—demonstrating significantly enhanced robustness and generalizability in real-world deployment.
Technology Category
Application Category
📝 Abstract
Measuring the connectivity of water in rivers and streams is essential for effective water resource management. Increased extreme weather events associated with climate change can result in alterations to river and stream connectivity. While traditional stream flow gauges are costly to deploy and limited to large river bodies, trail camera methods are a low-cost and easily deployed alternative to collect hourly data. Image capturing, however requires stream ecologists to manually curate (select and label) tens of thousands of images per year. To improve this workflow, we developed an automated instream trail camera image classification system consisting of three parts: (1) image processing, (2) image augmentation and (3) machine learning. The image preprocessing consists of seven image quality filters, foliage-based luma variance reduction, resizing and bottom-center cropping. Images are balanced using variable amount of generative augmentation using diffusion models and then passed to a machine learning classification model in labeled form. By using the vision transformer architecture and temporal image enhancement in our framework, we are able to increase the 75% base accuracy to 90% for a new unseen site image. We make use of a dataset captured and labeled by staff from the Connecticut Department of Energy and Environmental Protection between 2018-2020. Our results indicate that a combination of temporal image processing and attention-based models are effective at classifying unseen river connectivity images.
Problem
Research questions and friction points this paper is trying to address.
Automated River Connectivity
Efficiency Improvement
Accuracy Enhancement in Water Flow Measurement
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automatic Classification System
Image Processing
Machine Learning
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