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Open problems in Remote sensing

Bias and fairness across ecological regions in remote sensing models1 paper raises it · 1 addresses it
Aggregate metrics hide systematic performance disparities, leading to unreliable models for underrepresented biomes.
- FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Modelsraises · addresses
Generalization across heterogeneous sensors and conditions2 papers raise it · 2 address it
Models trained on single datasets fail to adapt to diverse sensing conditions, limiting real-world applicability.
- GeoSET: Generalist Foundation Model for SAR-to-EO Image Translationraises · addresses
- GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observationsraises · addresses
Interpretable spectral-spatial attribution for multispectral data1 paper raises it · 1 addresses it
Existing post-hoc explanations separate spatial and channel relevance, missing localized physical meaning in multispectral imagery.
- Spectral-Spatial Interpretationraises · addresses
Lack of data-centric benchmarks and guidelines for remote sensing VLMs1 paper raises it · 1 addresses it
The shift to vision-language models in remote sensing lacks systematic data-centric surveys and best practices.
- Data-Driven Vision-Language Models for Remote Sensing: A Surveyraises · addresses
Reproducibility and versioning in applied remote sensing systems1 paper raises it · 1 addresses it
Closed or non-versioned systems limit scientific reuse and independent benchmarking of recommendation models.
- Technical Report on the Turba Fertilizer Machine Learning Stack in Moroccoraises · addresses

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remote sensing · multimodal · object detection111 papers17 this week
hyperspectral images · hyperspectral unmixing · hyperspectral imaging57 papers7 this week
precision agriculture · crop disease · crop55 papers6 this week
remote sensing · wildfire detection · forests55 papers2 this week

Topics in this field

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arXiv cs.CVOct 14

OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation

Yini Li, Joanne Lin, David Blackham, David Bull, Nantheera Anantrasirichai

Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existing public datasets exhibit several limitations, including restricted motion diversity, limited scene coverage, weak degradations, reliance on auxiliary modalities such as event data, and misalignment between low-light inputs and ground truth. Furthermore, current datasets typically address either low-light enhancement or scene understanding, whereas these tasks should be considered jointly. We introduce \textbf{OLiVES}, a benchmark dataset for outdoor low-light videos with dynamic scenes, designed to support both low-light video enhancement (LLVE) and video object segmentation (VOS). The dataset addresses key content gaps by incorporating a diverse range of natural textures, animals, occlusions, dynamic activities, and close-up scenes. To the best of our knowledge, OLiVES is the first outdoor low-light video dataset captured using professional cinema-grade cameras. It provides over 25,000 aligned normal- and low-light frame pairs, along with over 46,000 VOS annotations. We benchmark state-of-the-art LLVE and VOS methods on OLiVES…

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arXiv cs.CVOct 22
Data-Driven Vision-Language Models for Remote Sensing: A Survey
Yue Zhou, Shujun Zhao, Xue Yang, Ruigang Li, Tianwen Zhang +5
During the deep learning era, innovations in remote sensing (RS) vision models primarily focused on optimizing network architectures for specific tasks and conducting end-to-end training. However, with the advent of the Vision-Language Models (VLMs) era, constrained by substantial computational resource demands, the mainstream research paradigm has shifted towards adapting general-purpose VLMs using RS vertical domain data for fine-tuning, rather than modifying their core architectures. This transition marks a change in the innovation pathway from model-centric to data-centric. Despite the increasing importance of data, the academic community still lacks a systematic, data-centric survey to guide researchers in constructing high-quality RS vision-language datasets for various application scenarios and in conducting effective model fine-tuning and performance evaluation based on these datasets. To address this gap, this paper presents a systematic review of the development status of RS datasets tailored for VLMs. It further provides a comprehensive practical guide for RS researchers in the large model era, covering the entire pipeline from data construction to fine-tuning and…
PDF arXiv
arXiv cs.CVOct 22
WovenAnchor Matcher: Specialized Intra- and Inter-Image Context Modeling for Feature Matching
Zhiyang Li, Ruijiang Jin, Thibaut Klenke, Yusuke Sekikawa, Nakamasa Inoue
Semi-dense local feature matching commonly aggregates contextual information before coarse-to-fine correspondence estimation. However, intra-image and inter-image aggregation serve different roles: the former should propagate spatial evidence within each image while retaining structured two-dimensional dependencies, whereas the latter should gather information from plausible corresponding regions while limiting noise from unrelated locations. We propose WovenAnchor Matcher (WAM), a semi-dense matching framework based on role-specialized context aggregation. WAM introduces two complementary operators. WovenMamba performs intra-image aggregation through horizontal-then-vertical state-space propagation, allowing vertical updates to operate on horizontally contextualized features and thereby encouraging a structured two-dimensional receptive-field bias. AnchorCrossAttention performs inter-image aggregation by using cross-attention to gather context from plausible corresponding regions across the image pair. It performs this exchange over local representative anchors that aggregate feature evidence within each window. On MegaDepth, WAM achieves 66.1 pose AUC@5$^\circ$, improving on…
PDF arXiv
arXiv cs.LGOct 22
Wavenumber-Aware Deep Learning for Robust Analysis of Raman Spectra
Jihwan Kim, Jaeseong Heo, Yujin Kim, Sang Bin Yoon, Chanjoong Kim +5
Machine-learning models for spectroscopy usually treat a spectrum as a fixed-grid intensity vector, leaving the physical coordinate implicit in the array index. Such models are tied to a single sampling grid, spectral range and resolution, which limits their use when measurement conditions change. Here we represent each spectral point as a pair of intensity and wavenumber and compute Transformer positional encodings from the wavenumber itself. On Raman spectra of three bacterial species and their synthetic mixtures, this representation remained robust to noise, sparser sampling and partial spectral coverage across convolutional, residual and Transformer networks. In controlled comparisons, wavenumber-based encoding outperformed index-based and learned encodings under partial observation and retained its performance on sampling grids never seen during training, where index-based encoding degraded. These findings were reproduced on a public 30-isolate bacterial benchmark. Explicit physical coordinates offer a simple route to robust spectral analysis under variable measurement conditions.
PDF arXiv
arXiv cs.LGOct 21
GANet: A Lightweight Group Attention Network for Plant Disease Identification on Embedded Systems
Anubhab Tripathi, Bibek Das, Asfak Ali, Sheli Shinha Chaudhuri
Global warming and climate change are major catalysts for plant diseases, significantly affecting food production worldwide. Addressing this issue requires continuous monitoring and early detection of plant diseases. While recent advancements in deep learning offer potential solutions, most models demand high processing power and struggle to perform efficiently on low-end electronic hardware. Furthermore, many existing works focus on specific crop diseases, limiting their broader applicability. This paper presents a lightweight Group Equivariance Attention Network (GANet) designed for the prediction and monitoring of 38 types of plant diseases. GANet introduces a novel Group Attention block and Group-Wise Feature Fusion block, making it both sparse and efficient. With only 0.001M parameters, the model achieves an impressive 99.99% accuracy on the PlantVillage dataset. Additionally, GANet is optimized for deployment on edge devices such as the Raspberry Pi 4b and Jetson Nano, where it reaches 5 FPS and 11 FPS, respectively, demonstrating its potential for real-time plant disease monitoring on low-power devices.
PDF arXiv
arXiv cs.CVOct 21
Multiclass Boundary Refinement with Neural Cellular Automata for Remote Sensing Land Cover Segmentation
Pascoassis Souza Santos Meira, Gilson Antonio Giraldi, Rafael Duarte Coelho dos Santos
Semantic segmentation of remote sensing imagery often places class boundaries a few pixels away from where they truly lie, and this position error is concentrated in the thin band measured by Boundary IoU (BIoU). We treat boundary refinement as the iterated application of a small learned local rule: the Boundary Refinement Neural Cellular Automaton (BRNCA), that starts from the class probabilities of a frozen, pre-trained segmenter and corrects them cell by cell. We generalise the binary rNCA to the multiclass case by keeping the visible channels on the probability simplex through an Aitchison (log-space) update and by adding free hidden channels. The best configuration improves the mean BIoU over the base segmenter by 2.36 points (from 46.63 to 48.99). The gain is concentrated on the irregular woodland class (+8.5 points BIoU); this suggests that position error is correlated with the fractal dimension of the class boundaries.
PDF arXiv
arXiv cs.CVOct 21
Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative Study
Alberto De Zan, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto
This paper presents a novel strategy for UAV detection through visible (RGB) and infrared (IR) fusion, in order to improve accuracy and precision in harsh environments and weather conditions. In this research, two opposite approaches have been studied and implemented: the first concerns image-level (early) fusion, which merges two different spectra into a single image to which one detector is applied. On the other hand, the second approach, called decision-level (late) fusion, focuses on blending together the output bounding boxes of two different detection pipelines, aiming to create a common result that can be applied to both spectra simultaneously. Both strategies have been extensively tested in a dedicated environment, which included alternating low and high brightness scenes, different backgrounds (sky and ground), distances (5, 25 and 60 m), altitudes and zoom levels (1X, 2X and 4X). These tests allowed for the assessment of actual detection capabilities and system reliability in real-world applications. Furthermore, the two fusion strategies were evaluated and compared over a set of aligned images in order to highlight the better approach of the two. The results indicate…
PDF arXiv

Papers

Topics in this field

remote sensing · multimodal · object detection111 papers17 this week
hyperspectral images · hyperspectral unmixing · hyperspectral imaging57 papers7 this week
precision agriculture · crop disease · crop55 papers6 this week
remote sensing · wildfire detection · forests55 papers2 this week

★ Spotlight this month

arXiv cs.CVOct 14

OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation

Yini Li, Joanne Lin, David Blackham, David Bull, Nantheera Anantrasirichai

Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existing public datasets exhibit several limitations, including restricted motion diversity, limited scene coverage, weak degradations, reliance on auxiliary modalities such as event data, and misalignment between low-light inputs and ground truth. Furthermore, current datasets typically address either low-light enhancement or scene understanding, whereas these tasks should be considered jointly. We introduce \textbf{OLiVES}, a benchmark dataset for outdoor low-light videos with dynamic scenes, designed to support both low-light video enhancement (LLVE) and video object segmentation (VOS). The dataset addresses key content gaps by incorporating a diverse range of natural textures, animals, occlusions, dynamic activities, and close-up scenes. To the best of our knowledge, OLiVES is the first outdoor low-light video dataset captured using professional cinema-grade cameras. It provides over 25,000 aligned normal- and low-light frame pairs, along with over 46,000 VOS annotations. We benchmark state-of-the-art LLVE and VOS methods on OLiVES…

PDF arXiv page Details

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.CVOct 22
Data-Driven Vision-Language Models for Remote Sensing: A Survey
Yue Zhou, Shujun Zhao, Xue Yang, Ruigang Li, Tianwen Zhang +5
During the deep learning era, innovations in remote sensing (RS) vision models primarily focused on optimizing network architectures for specific tasks and conducting end-to-end training. However, with the advent of the Vision-Language Models (VLMs) era, constrained by substantial computational resource demands, the mainstream research paradigm has shifted towards adapting general-purpose VLMs using RS vertical domain data for fine-tuning, rather than modifying their core architectures. This transition marks a change in the innovation pathway from model-centric to data-centric. Despite the increasing importance of data, the academic community still lacks a systematic, data-centric survey to guide researchers in constructing high-quality RS vision-language datasets for various application scenarios and in conducting effective model fine-tuning and performance evaluation based on these datasets. To address this gap, this paper presents a systematic review of the development status of RS datasets tailored for VLMs. It further provides a comprehensive practical guide for RS researchers in the large model era, covering the entire pipeline from data construction to fine-tuning and…
PDF arXiv
arXiv cs.CVOct 22
WovenAnchor Matcher: Specialized Intra- and Inter-Image Context Modeling for Feature Matching
Zhiyang Li, Ruijiang Jin, Thibaut Klenke, Yusuke Sekikawa, Nakamasa Inoue
Semi-dense local feature matching commonly aggregates contextual information before coarse-to-fine correspondence estimation. However, intra-image and inter-image aggregation serve different roles: the former should propagate spatial evidence within each image while retaining structured two-dimensional dependencies, whereas the latter should gather information from plausible corresponding regions while limiting noise from unrelated locations. We propose WovenAnchor Matcher (WAM), a semi-dense matching framework based on role-specialized context aggregation. WAM introduces two complementary operators. WovenMamba performs intra-image aggregation through horizontal-then-vertical state-space propagation, allowing vertical updates to operate on horizontally contextualized features and thereby encouraging a structured two-dimensional receptive-field bias. AnchorCrossAttention performs inter-image aggregation by using cross-attention to gather context from plausible corresponding regions across the image pair. It performs this exchange over local representative anchors that aggregate feature evidence within each window. On MegaDepth, WAM achieves 66.1 pose AUC@5$^\circ$, improving on…
PDF arXiv
arXiv cs.LGOct 22
Wavenumber-Aware Deep Learning for Robust Analysis of Raman Spectra
Jihwan Kim, Jaeseong Heo, Yujin Kim, Sang Bin Yoon, Chanjoong Kim +5
Machine-learning models for spectroscopy usually treat a spectrum as a fixed-grid intensity vector, leaving the physical coordinate implicit in the array index. Such models are tied to a single sampling grid, spectral range and resolution, which limits their use when measurement conditions change. Here we represent each spectral point as a pair of intensity and wavenumber and compute Transformer positional encodings from the wavenumber itself. On Raman spectra of three bacterial species and their synthetic mixtures, this representation remained robust to noise, sparser sampling and partial spectral coverage across convolutional, residual and Transformer networks. In controlled comparisons, wavenumber-based encoding outperformed index-based and learned encodings under partial observation and retained its performance on sampling grids never seen during training, where index-based encoding degraded. These findings were reproduced on a public 30-isolate bacterial benchmark. Explicit physical coordinates offer a simple route to robust spectral analysis under variable measurement conditions.
PDF arXiv
arXiv cs.LGOct 21
GANet: A Lightweight Group Attention Network for Plant Disease Identification on Embedded Systems
Anubhab Tripathi, Bibek Das, Asfak Ali, Sheli Shinha Chaudhuri
Global warming and climate change are major catalysts for plant diseases, significantly affecting food production worldwide. Addressing this issue requires continuous monitoring and early detection of plant diseases. While recent advancements in deep learning offer potential solutions, most models demand high processing power and struggle to perform efficiently on low-end electronic hardware. Furthermore, many existing works focus on specific crop diseases, limiting their broader applicability. This paper presents a lightweight Group Equivariance Attention Network (GANet) designed for the prediction and monitoring of 38 types of plant diseases. GANet introduces a novel Group Attention block and Group-Wise Feature Fusion block, making it both sparse and efficient. With only 0.001M parameters, the model achieves an impressive 99.99% accuracy on the PlantVillage dataset. Additionally, GANet is optimized for deployment on edge devices such as the Raspberry Pi 4b and Jetson Nano, where it reaches 5 FPS and 11 FPS, respectively, demonstrating its potential for real-time plant disease monitoring on low-power devices.
PDF arXiv
arXiv cs.CVOct 21
Multiclass Boundary Refinement with Neural Cellular Automata for Remote Sensing Land Cover Segmentation
Pascoassis Souza Santos Meira, Gilson Antonio Giraldi, Rafael Duarte Coelho dos Santos
Semantic segmentation of remote sensing imagery often places class boundaries a few pixels away from where they truly lie, and this position error is concentrated in the thin band measured by Boundary IoU (BIoU). We treat boundary refinement as the iterated application of a small learned local rule: the Boundary Refinement Neural Cellular Automaton (BRNCA), that starts from the class probabilities of a frozen, pre-trained segmenter and corrects them cell by cell. We generalise the binary rNCA to the multiclass case by keeping the visible channels on the probability simplex through an Aitchison (log-space) update and by adding free hidden channels. The best configuration improves the mean BIoU over the base segmenter by 2.36 points (from 46.63 to 48.99). The gain is concentrated on the irregular woodland class (+8.5 points BIoU); this suggests that position error is correlated with the fractal dimension of the class boundaries.
PDF arXiv
arXiv cs.CVOct 21
Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative Study
Alberto De Zan, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto
This paper presents a novel strategy for UAV detection through visible (RGB) and infrared (IR) fusion, in order to improve accuracy and precision in harsh environments and weather conditions. In this research, two opposite approaches have been studied and implemented: the first concerns image-level (early) fusion, which merges two different spectra into a single image to which one detector is applied. On the other hand, the second approach, called decision-level (late) fusion, focuses on blending together the output bounding boxes of two different detection pipelines, aiming to create a common result that can be applied to both spectra simultaneously. Both strategies have been extensively tested in a dedicated environment, which included alternating low and high brightness scenes, different backgrounds (sky and ground), distances (5, 25 and 60 m), altitudes and zoom levels (1X, 2X and 4X). These tests allowed for the assessment of actual detection capabilities and system reliability in real-world applications. Furthermore, the two fusion strategies were evaluated and compared over a set of aligned images in order to highlight the better approach of the two. The results indicate…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 41m ago

Remote sensing

Found by the topic model: remote sensing, hyperspectral images, precision agriculture.

32papers this week−48.4% vs last week

All papers · 278 With code · 6

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214 papers this month

Open problems in Remote sensing

Bias and fairness across ecological regions in remote sensing models1 paper raises it · 1 addresses it
Aggregate metrics hide systematic performance disparities, leading to unreliable models for underrepresented biomes.
- FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Modelsraises · addresses
Generalization across heterogeneous sensors and conditions2 papers raise it · 2 address it
Models trained on single datasets fail to adapt to diverse sensing conditions, limiting real-world applicability.
- GeoSET: Generalist Foundation Model for SAR-to-EO Image Translationraises · addresses
- GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observationsraises · addresses
Interpretable spectral-spatial attribution for multispectral data1 paper raises it · 1 addresses it
Existing post-hoc explanations separate spatial and channel relevance, missing localized physical meaning in multispectral imagery.
- Spectral-Spatial Interpretationraises · addresses
Lack of data-centric benchmarks and guidelines for remote sensing VLMs1 paper raises it · 1 addresses it
The shift to vision-language models in remote sensing lacks systematic data-centric surveys and best practices.
- Data-Driven Vision-Language Models for Remote Sensing: A Surveyraises · addresses
Reproducibility and versioning in applied remote sensing systems1 paper raises it · 1 addresses it
Closed or non-versioned systems limit scientific reuse and independent benchmarking of recommendation models.
- Technical Report on the Turba Fertilizer Machine Learning Stack in Moroccoraises · addresses

Named by an AI from these papers' abstracts.