AquaWatch Visual Environmental Intelligence (AVEI)
Understanding Active Learning in Environmental AI
Building environmental visual AI is challenging due to the vast variability in natural scenes, which requires the AI to discern subtle changes and patterns under different lighting, weather conditions, and seasonal changes. Additionally, the need for extensive datasets to train robust models, that can accurately classify and react to environmental stimuli, poses a significant hurdle in terms of data collection and processing.
The inherent complexity of developing environmental visual AI is significantly mitigated by the AW-108's advanced imaging capabilities, which offer high-resolution data capture across diverse environmental conditions.
The AW-108 excels in consistently delivering quality images, crucial for training AI to recognize a wide array of environmental states and changes, from the subtle to the overt.
Its edge computing prowess enables on-device processing, which streamlines the data capture phase by pre-filtering and categorizing imagery before it's even sent for training, thus enhancing the efficiency and effectiveness of dataset compilation.
This means datasets are more targeted, less noisy, and contain the nuanced detail necessary for creating a finely tuned AI model capable of accurate and reliable environmental monitoring.
Powered by SnapCore, this sophisticated technology is not just capturing data and sharing data but is actively learning to safeguard our natural resources more efficiently. It’s not just the sophistication that matters for environmental monitoring, especially in an aquatic environment, it’s the practicality and reliability of the hardware. Our cameras have been deployed underwater for years at a time, they’ve spent up to six years continuously on Alaskan fishing boats. We can deploy them on cellular or satellite connectivity, from mains or solar power. They just work.
By recording high quality images, processing at point of capture they make life easier, and environmental outcomes better.
AVEI brings together the possibilities provided by the AW-108 along with our knowledge in environmental AI and takes our partners through the process of using visual data to solve real world environmental problems.
Defining the Problem: We start by identifying specific environmental challenges, such as Combined Sewer Overflows (CSOs), trash rack fouling, or discharge points, that require vigilant monitoring and swift response.
Data Gathering: Utilizing the AW-108’s 4K video capture, we collect high-fidelity visual data from various water systems, ensuring a comprehensive dataset that captures the nuances of environmental changes. Depending on the solution required, this can start from 100-1000 images but monitoring is possible during the data gathering process, adding immediate value from the hardware investment.
Model Training: With expert annotation of this visual data, we train robust machine learning models that can accurately detect early signs of environmental issues like CSOs.
Outcome Refinement: Our models are refined through iterative training to pinpoint desired outcomes, such as reducing pollution levels and mitigating health risks.
API and Insight Delivery: Insights gleaned from the data can be delivered via a user-friendly API, equipping stakeholders with real-time data to make informed decisions.
Ongoing Calibration and Support: The AW-108 system is designed for continuous improvement. With each event, our models learn and adapt, reducing the need for human intervention and enhancing precision over time.
Decoding Active Learning in AI:
Active learning stands at the forefront of AquaWatch's Environmental AI endeavors, involving a model that selects the most enlightening samples from the AW-108's data collection for labeling, enhancing the model’s efficiency. This strategic sample selection means the model requires fewer data to learn effectively, easing the typically labor-intensive image annotation process.
The Active Learning Cycle Explained:
The cycle begins with our AW-108 gathering a baseline of labeled data. It then enters an iterative phase, pinpointing which new data points to label, thereby minimizing the manual effort of data annotation. This is especially critical for extensive data sets, such as high-resolution environmental imagery.
Strategic Active Learning Pathways:
Our approach employs various query strategies within active learning, from uncertainty and diversity sampling to entropy-based techniques. These methods enable the AW-108 to identify which data points will most effectively train our Environmental AI models, whether it's for streamlining the monitoring of trash racks or detecting anomalies in water quality.
Active Learning in Action:
The process is dynamic, starting with a foundational dataset and progressively incorporating new, informative samples for manual labeling. Our models become more robust with each iteration, continually enhancing the accuracy of our environmental monitoring tools.
Active Learning Benefits:
By adopting active learning, AquaWatch significantly cuts down the costs and time associated with data labeling. This method not only hastens our time to deliver an end solution, but also ensures our models perform at peak efficiency, consuming less data while maintaining high accuracy.
Active learning is interlinked with semi-supervised learning, reinforcement learning, and transfer learning, all of which play a part in our Environmental AI development strategy. These methods collectively contribute to the advancement of our environmental monitoring solutions.
The Outcome and Actions:
Active learning propels AquaWatch's mission to deliver intelligent environmental monitoring solutions. By selecting the most informative data for our Environmental AI to learn from, we maintain or enhance model accuracy, ensuring our solutions are both effective and efficient.
As we face an ever-growing expanse of data, active learning stands as a pivotal tool in our environmental AI toolkit, accelerating our ability to protect our natural capital.