SmartScan: An AI-based Interactive Framework for Automated Region Extraction from Satellite Images

The deployment of a continuous methane monitoring system requires determining the optimal number and placement of fixed sensors. However, planning is labor-intensive, requiring extensive site setup and iteration to meet client restrictions. This challenge is amplified when evaluating multiple sites, limiting scalability. To address this, we introduce SmartScan, an AI framework that automates data extraction for optimal sensor placement. SmartScan identifies subspaces of interest from satellite images using an interactive tool to create facility-specific constraint sets efficiently. SmartScan leverages the Segment Anything Model (SAM), a prompt-based transformer for zero-shot segmentation, enabling subspace extraction without explicit training. It operates in two modes: (1) Data Curation Mode, where satellite images are processed to extract high-quality subspaces using an interactive prompting system for SAM, and (2) Autonomous Mode, where user-curated prompts train a deep learning network to replace manual prompting, fully automating subspace extraction. The interactive tool also serves for quality control, allowing users to refine AI-generated outputs and generate additional constraint sets as needed. With its AI-driven prompting mechanism, SmartScan delivers high-throughput, high-quality subspace extraction with minimal human intervention, enhancing scalability and efficiency. Notably, its adaptable design makes it suitable for extracting regions of interest from ultra-high-resolution satellite imagery across various domains.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering

Few-shot segmentation is the problem of learning to identify specific types of objects (e.g., airplanes) in images from a small set of labeled reference images. The current state of the art is driven by resource-intensive construction of models for every new domain-specific application. Such models must be trained on enormous labeled datasets of unrelated objects (e.g., cars, trains, animals) so that their ``knowledge'' can be transferred to new types of objects. In this paper, we show how to leverage existing vision foundation models (VFMs) to reduce the incremental cost of creating few-shot segmentation models for new domains. Specifically, we introduce SAMIC, a small network that learns how to prompt VFMs in order to segment new types of objects in domain-specific applications. SAMIC enables any task to be approached as a few-shot learning problem. At 2.6 million parameters, it is 94% smaller than the leading models (e.g., having ResNet 101 backbone with 45+ million parameters). Even using 1/5th of the training data provided by one-shot benchmarks, SAMIC is competitive with, or sets the state of the art, on a variety of few-shot and semantic segmentation datasets including COCO-$20^i$, Pascal-$5^i$, PerSeg, FSS-1000, and NWPU VHR-10.

Subspace-Constrained Continuous Methane Leak Monitoring and Optimal Sensor Placement

This work presents a procedure that can quickly identify and isolate methane emission sources leading to expedient remediation. Minimizing the time required to identify a leak and the subsequent time to dispatch repair crews can significantly reduce the amount of methane released into the atmosphere. The procedure developed utilizes permanently installed low-cost methane sensors at an oilfield facility to continuously monitor leaked gas concentration above background levels. The methods developed for optimal sensor placement and leak inversion in consideration of predefined subspaces and restricted zones are presented. In particular, subspaces represent regions comprising one or more equipment items that may leak, and restricted zones define regions in which a sensor may not be placed due to site restrictions by design. Thus, subspaces constrain the inversion problem to specified locales, while restricted zones constrain sensor placement to feasible zones. The development of synthetic wind models, and those based on historical data, are also presented as a means to accommodate optimal sensor placement under wind uncertainty. The wind models serve as realizations for planning purposes, with the aim of maximizing the mean coverage measure for a given number of sensors. Once the optimal design is established, continuous real-time monitoring permits localization and quantification of a methane leak source. The necessary methods, mathematical formulation and demonstrative test results are presented.