# Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features

arXiv: https://arxiv.org/abs/2306.05341 · HTML: https://mlsanity.com/paper/2306.05341/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Scale-Aware Trident Networks for Object Detection](https://mlsanity.com/paper/1901.01892/) (2019)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Sparse Instance Activation for Real-Time Instance Segmentation](https://mlsanity.com/paper/2203.12827/) (2022)
- [CenterPoly: real-time instance segmentation using bounding polygons](https://mlsanity.com/paper/2108.08923/) (2021)

## Built on by (most cited first, 5 of 5)

- [Assessment of a new GeoAI foundation model for flood inundation mapping](https://mlsanity.com/paper/2309.14500/) (2023)
- [Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability](https://mlsanity.com/paper/2409.00489/) (2024)
- [Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping](https://mlsanity.com/paper/2401.08787/) (2024)
- [A multi-scale vision transformer-based multimodal GeoAI model for mapping Arctic permafrost thaw](https://mlsanity.com/paper/2504.17822/) (2025)
- [Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks](https://mlsanity.com/paper/2309.03367/) (2023)

## Abstract (arXiv)

This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this analysis. To achieve real-time prediction, our workflow employs a lightweight, deep learning-based instance segmentation model, SparseInst, which introduces and uses Instance Activation Maps to accurately locate the position of objects within the image scene. Experimental results show that the model can achieve better accuracy of prediction at a much faster inference speed than the popular Mask-RCNN model.