PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs

• URL: http://arxiv.org/abs/2601.11451v1
• Date: Fri, 16 Jan 2026 17:16:26 GMT
• Title: PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs
• Authors: Oishee Bintey Hoque, Nibir Chandra Mandal, Kyle Luong, Amanda Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga,
• Abstract summary: Large-scale livestock operations pose significant risks to human health and the environment.<n>We present an infrastructure-first, explainable pipeline for identifying and characterizing Concentrated Animal Feeding Operations from aerial and satellite imagery.
• Score: 8.991143286122615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases and extreme weather events. As the number of such operations continues to grow, accurate and scalable mapping has become increasingly important. In this work, we present an infrastructure-first, explainable pipeline for identifying and characterizing Concentrated Animal Feeding Operations (CAFOs) from aerial and satellite imagery. Our method (1) detects candidate infrastructure (e.g., barns, feedlots, manure lagoons, silos) with a domain-tuned YOLOv8 detector, then derives SAM2 masks from these boxes and filters component-specific criteria, (2) extracts structured descriptors (e.g., counts, areas, orientations, and spatial relations) and fuses them with deep visual features using a lightweight spatial cross-attention classifier, and (3) outputs both CAFO type predictions and mask-level attributions that link decisions to visible infrastructure. Through comprehensive evaluation, we show that our approach achieves state-of-the-art performance, with Swin-B+PRISM-CAFO surpassing the best performing baseline by up to 15\%. Beyond strong predictive performance across diverse U.S. regions, we run systematic gradient--activation analyses that quantify the impact of domain priors and show ho

Related papers

• Habitat Classification from Ground-Level Imagery Using Deep Neural Networks [1.3408365072149797]
  This study applies state-of-the-art deep neural network architectures to ground-level habitat imagery.<n>We evaluate two families of models -- convolutional neural networks (CNNs) and vision transformers (ViTs)<n>Our results demonstrate that ViTs consistently outperform state-of-the-art CNN baselines on key classification metrics.
  arXiv  Detail & Related papers  (2025-07-05T12:07:13Z)
• Backdoor Cleaning without External Guidance in MLLM Fine-tuning [76.82121084745785]
  Believe Your Eyes (BYE) is a data filtering framework that leverages attention entropy patterns as self-supervised signals to identify and filter backdoor samples.<n>It achieves near-zero attack success rates while maintaining clean-task performance.
  arXiv  Detail & Related papers  (2025-05-22T17:11:58Z)
• Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection [11.497620257835964]
  We propose CCKT-Det trained without any extra supervision.<n>The proposed framework constructs a cyclic and dynamic knowledge transfer from language queries and visual region features extracted from vision-language models (VLMs)<n> CCKT-Det can consistently improve performance as the scale of VLMs increases, all while requiring the detector at a moderate level of overhead.
  arXiv  Detail & Related papers  (2025-03-14T02:04:28Z)
• ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
  The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation. SAM's Transformer-based structure prioritizes global and low-frequency information. CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-06-30T14:55:32Z)
• FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2 [41.28284355136163]
  Wildfires pose significant threats to human and animal lives, ecosystems, and socio-economic stability. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area) This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas.
  arXiv  Detail & Related papers  (2023-11-06T18:42:05Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers [79.60022233109397]
  We present spatial prior attention (SPAN), a framework that takes advantage of consistent spatial and semantic structure in unlabeled image datasets. SPAN operates by regularizing attention masks from separate transformer heads to follow various priors over semantic regions. We find that the resulting attention masks are more interpretable than those derived from domain-agnostic pretraining.
  arXiv  Detail & Related papers  (2022-09-07T02:30:36Z)
• Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms [0.0]
  We demonstrate a process for rapid identification of significant structural expansion using satellite imagery. We combine state-of-the-art building segmentation with a likelihood-based change-point detection model to provide a robust signal of building expansion.
  arXiv  Detail & Related papers  (2021-05-29T00:33:18Z)
• Unveiling the Potential of Structure-Preserving for Weakly Supervised Object Localization [71.79436685992128]
  We propose a two-stage approach, termed structure-preserving activation (SPA), towards fully leveraging the structure information incorporated in convolutional features for WSOL. In the first stage, a restricted activation module (RAM) is designed to alleviate the structure-missing issue caused by the classification network. In the second stage, we propose a post-process approach, termed self-correlation map generating (SCG) module to obtain structure-preserving localization maps.
  arXiv  Detail & Related papers  (2021-03-08T03:04:14Z)
• PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation [87.50205728818601]
  We propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information.
  arXiv  Detail & Related papers  (2020-11-25T11:03:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.