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

arXiv: https://arxiv.org/abs/2504.00200 · HTML: https://mlsanity.com/paper/2504.00200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/1906.00817/) (2019)
- [Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem](https://mlsanity.com/paper/1810.01940/) (2018)
- [Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames](https://mlsanity.com/paper/1811.12607/) (2018)
- [PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization](https://mlsanity.com/paper/2211.06560/) (2022)
- [Estimating Uncertainty in Landslide Segmentation Models](https://mlsanity.com/paper/2311.11138/) (2023)
- [Thermal Analysis for NVIDIA GTX480 Fermi GPU Architecture](https://mlsanity.com/paper/2403.16239/) (2024)
- [SAMIC: Segment Anything with In-Context Spatial Prompt Engineering](https://mlsanity.com/paper/2412.11998/) (2024)
- [Emotion Recognition from the perspective of Activity Recognition](https://mlsanity.com/paper/2403.16263/) (2024)
- [Subspace-Constrained Continuous Methane Leak Monitoring and Optimal Sensor Placement](https://mlsanity.com/paper/2308.01836/) (2023)

## Abstract (arXiv)

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.