TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock

• URL: http://arxiv.org/abs/2604.09648v1
• Date: Fri, 27 Mar 2026 19:30:06 GMT
• Title: TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock
• Abstract summary: Quantifying exhaled CO2 from free-roaming cattle is both a direct indicator of rumen metabolic state and a prerequisite for farm-scale carbon accounting.<n>We present TRACE, the first unified framework to jointly address per-frame CO2 plume segmentation and clip-level emission flux classification.
• Score: 4.15623340386296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantifying exhaled CO2 from free-roaming cattle is both a direct indicator of rumen metabolic state and a prerequisite for farm-scale carbon accounting, yet no existing system can deliver continuous, spatially resolved measurements without physical confinement or contact. We present TRACE (Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock), the first unified framework to jointly address per-frame CO2 plume segmentation and clip-level emission flux classification from mid-wave infrared (MWIR) thermal video. TRACE contributes three domain-specific advances: a Thermal Gas-Aware Attention (TGAA) encoder that incorporates per-pixel gas intensity as a spatial supervisory signal to direct self-attention toward high-emission regions at each encoder stage; an Attention-based Temporal Fusion (ATF) module that captures breath-cycle dynamics through structured cross-frame attention for sequence-level flux classification; and a four-stage progressive training curriculum that couples both objectives while preventing gradient interference. Benchmarked against fifteen state-of-the-art models on the CO2 Farm Thermal Gas Dataset, TRACE achieves an mIoU of 0.998 and the best result on every segmentation and classification metric simultaneously, outperforming domain-specific gas segmenters with several times more parameters and surpassing all baselines in flux classification. Ablation studies confirm that each component is individually essential: gas-conditioned attention alone determines precise plume boundary localization, and temporal reasoning is indispensable for flux-level discrimination. TRACE establishes a practical path toward non-invasive, continuous, per-animal CO2 monitoring from overhead thermal cameras at commercial scale. Codes are available at https://github.com/taminulislam/trace.

Related papers

• Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations [58.079925240809644]
  We propose an end-to-end graph neural surrogate tailored to CO$$ibility plume migration forecasting in geological storage.<n>The method is evaluated on the SPE11A benchmark, a well-known industry test case designed to assess CO$$ storage scenarios.<n>The proposed model produces competitive forecasts of gas saturation and liquid-phase density, with cumulative errors that remain moderate over extended forecasting horizons.
  arXiv  Detail & Related papers  (2026-06-15T18:19:17Z)
• CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction [29.92121214550302]
  Methane is a potent greenhouse gas that significantly contributes to global warming.<n>We propose Contrastive Hierarchical Meta-network (CHAM-net), a framework that explicitly learns from historical context to capture site-specific dynamics.<n>CHAM-net consistently outperforms all baseline methods on both simulation and observational datasets for methane emission and consumption.
  arXiv  Detail & Related papers  (2026-05-29T20:26:26Z)
• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• Pay Attention to CTC: Fast and Robust Pseudo-Labelling for Unified Speech Recognition [61.39209522608919]
  Unified Speech Recognition has emerged as a semi-supervised framework for training a single model for audio, visual, and audiovisual speech recognition.<n>We propose CTC-driven teacher forcing, where greedily decoded CTC pseudo-labels are fed into the decoder to generate attention targets.<n>Because CTC and CTC-driven attention pseudo-labels have the same length, the decoder can predict both simultaneously.
  arXiv  Detail & Related papers  (2026-02-22T19:38:21Z)
• From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark [67.29937933325849]
  Operando IR Prediction aims to forecast the time-resolved evolution of spectral fingerprints'' from a single static spectrum.<n>OpIRSpec-7K comprises 7,118 high-quality samples across 10 distinct battery systems.<n>ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines.
  arXiv  Detail & Related papers  (2026-02-20T18:58:43Z)
• Standardized Methods and Recommendations for Green Federated Learning [4.07505182773706]
  Federated learning (FL) enables collaborative model training over privacy-sensitive, distributed data, but its environmental impact is difficult to compare across studies.<n>We present a practical carbon-accounting methodology for FL CO2e tracking using NVIDIA NVFlare and CodeCarbon.
  arXiv  Detail & Related papers  (2026-01-30T21:46:36Z)
• FUME: Fused Unified Multi-Gas Emission Network for Livestock Rumen Acidosis Detection [3.7515646463759698]
  Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns.<n>We present FUME, the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions.<n>Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems.
  arXiv  Detail & Related papers  (2026-01-13T04:17:22Z)
• COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy [15.886563559077507]
  Covalent organic frameworks (COFs) are promising adsorbents for gas adsorbing and separation.<n>A universal COFs adsorbing prediction framework (COFAP) is proposed, which can extract multi-modal structural and chemical features through deep learning.<n>A weight-adjustable prioritization scheme is also developed to enable flexible, application-specific ranking of candidate COFs for researchers.
  arXiv  Detail & Related papers  (2025-11-03T10:11:33Z)
• CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging [4.567122178196833]
  Carbon dioxide (CO$$) emissions are critical indicators of both environmental impact and industrial processes.<n>We introduce CarboFormer, a lightweight semantic segmentation framework for Optical Gas Imaging (OGI)<n>Our approach integrates an optimized encoder-decoder architecture with specialized multi-scale feature fusion and auxiliary supervision strategies.
  arXiv  Detail & Related papers  (2025-05-23T18:01:42Z)
• Degree-Conscious Spiking Graph for Cross-Domain Adaptation [51.58506501415558]
  Spiking Graph Networks (SGNs) have demonstrated significant potential in graph classification.<n>We introduce a novel framework named Degree-Consicious Spiking Graph for Cross-Domain Adaptation (DeSGraDA)<n>DeSGraDA enhances generalization across domains with three key components.
  arXiv  Detail & Related papers  (2024-10-09T13:45:54Z)
• Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators [58.728312684306545]
  Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale.
  arXiv  Detail & Related papers  (2022-10-31T04:04:03Z)
• SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment [76.80165589520385]
  We name our method SEA (SEmantic-aware Alignment) distillation given the nature of abstracting dense fine-grained information. It achieves new state-of-the-art results on the challenging object detection task on both one- and two-stage detectors.
  arXiv  Detail & Related papers  (2022-03-02T04:24:05Z)
• Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition [65.78703941973183]
  We propose a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder. Specifically, the CD-JBF-GC can explore the motion transmission between the joint stream and the bone stream. The pose prediction based auto-encoder in the self-supervised training stage allows the network to learn motion representation from unlabeled data.
  arXiv  Detail & Related papers  (2022-02-08T16:03:15Z)

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.