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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 across biomes, limiting real-world reliability.
- FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Modelsraises · addresses
Generalist models for heterogeneous remote sensing tasks2 papers raise it · 2 address it
Specialized models for single datasets or conditions limit reuse across sensors, resolutions, and modalities.
- 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
Separate spatial or channel-wise explanations fail to capture localized, physically meaningful relevance in multispectral imagery.
- Spectral-Spatial Interpretationraises · addresses
Reproducible and benchmarkable site-specific recommendation systems1 paper raises it · 1 addresses it
Lack of versioned, loadable, and independently benchmarkable models constrains scientific reuse and validation.
- Technical Report on the Turba Fertilizer Machine Learning Stack in Moroccoraises · addresses
Data-centric pipelines for remote sensing vision-language models1 paper raises it · 1 addresses it
Lack of systematic guidance for constructing high-quality datasets and fine-tuning VLMs hinders progress in the field.
- Data-Driven Vision-Language Models for Remote Sensing: A Surveyraises · addresses

Named by an AI from these papers' abstracts.

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remote sensing · multimodal · object detection114 papers19 this week
hyperspectral images · hyperspectral unmixing · hyperspectral imaging62 papers11 this week
precision agriculture · crop disease · crop58 papers9 this week
remote sensing · wildfire detection · forests56 papers2 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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arXiv physics.soc-phOct 12
Popularity Trajectories and Mixing Patterns of Urban Venues in Two European Cities
Wang Ngai Yeung, Lakmal Meegahapola, Daniele Quercia, Riccardo Di Clemente
Urban venues such as restaurants, shops, bars and parks host the daily life of a city, and their collective rise and fall signals how vibrant a neighbourhood is. The usual assumption is that a venue's fortunes follow its neighbourhood's, so that how wealthy an area is and how many people live there will decide a venue's performance. Yet whether there is a common performance trajectory shared by venues over time is underexplored. In this study, we use 5.5 years of Foursquare data for about 10^4 venues in London and Barcelona. We found six distinguishable shapes recur in both cities, that are (1) Fall, (2) Rise, (3) Rise & Fall, (4) Steady, (5) Ephemeral (an early hit that loses popularity fast) and (6) Underdogs (a slow starter that gains popularity late). However, no recovery shape appears, that is neither city yields a U-shaped fall-and-rise group. Additionally, we found that deprivation and population density are not indicative which shape a venue follows. We also discovered that how early a shape can be recognized depends on when its distinctive segment occurs, not a shape's complexity. Proximity between venues, however, does predict how well-performing a venue is. We…
PDF arXiv
arXiv cs.NEOct 12
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation
Bahrul Ilmi Nasution, Rezzy Eko Caraka, Robert Kurniawan
We present an overview of R package called \texttt{naspaclust}, a tool for spatial fuzzy clustering improvement by hybridising the Fuzzy Geographically Weighted Clustering (FGWC) with nature-inspired optimisation algorithms. There are seven optimisation algorithms in general, namely Artificial Bee Colony, Flower Pollination Algorithm, Gravitational Search Algorithm, Harris-Hawk Optimisation, Intelligent Firefly Algorithm, Particle Swarm Optimisation, and Teaching-Learning Based optimisation. The optimisation algorithms available do not only use the basic algorithms, but also the modified version of several parts such as randomisation terms and updating rules. Furthermore, we also provide additional options by providing more distance metrics options based on the \texttt{ rdist} package. This study showed the implementation of the \texttt{naspaclust} package in various cases, as well as comparing the performance of the algorithms in the scope of basic algorithms. The \texttt{naspaclust} package allows to improve the performance of fuzzy clustering, mainly in spatial context.

Papers

Topics in this field

remote sensing · multimodal · object detection114 papers19 this week
hyperspectral images · hyperspectral unmixing · hyperspectral imaging62 papers11 this week
precision agriculture · crop disease · crop58 papers9 this week
remote sensing · wildfire detection · forests56 papers2 this week

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 1m ago

Remote sensing

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

41papers this week−33.9% vs last week

All papers · 290 With code · 7

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225 papers this month

Recent papers

All papers in Research

arXiv physics.soc-phOct 12
Popularity Trajectories and Mixing Patterns of Urban Venues in Two European Cities
Wang Ngai Yeung, Lakmal Meegahapola, Daniele Quercia, Riccardo Di Clemente
Urban venues such as restaurants, shops, bars and parks host the daily life of a city, and their collective rise and fall signals how vibrant a neighbourhood is. The usual assumption is that a venue's fortunes follow its neighbourhood's, so that how wealthy an area is and how many people live there will decide a venue's performance. Yet whether there is a common performance trajectory shared by venues over time is underexplored. In this study, we use 5.5 years of Foursquare data for about 10^4 venues in London and Barcelona. We found six distinguishable shapes recur in both cities, that are (1) Fall, (2) Rise, (3) Rise & Fall, (4) Steady, (5) Ephemeral (an early hit that loses popularity fast) and (6) Underdogs (a slow starter that gains popularity late). However, no recovery shape appears, that is neither city yields a U-shaped fall-and-rise group. Additionally, we found that deprivation and population density are not indicative which shape a venue follows. We also discovered that how early a shape can be recognized depends on when its distinctive segment occurs, not a shape's complexity. Proximity between venues, however, does predict how well-performing a venue is. We…
PDF arXiv
arXiv cs.NEOct 12
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation
Bahrul Ilmi Nasution, Rezzy Eko Caraka, Robert Kurniawan
We present an overview of R package called \texttt{naspaclust}, a tool for spatial fuzzy clustering improvement by hybridising the Fuzzy Geographically Weighted Clustering (FGWC) with nature-inspired optimisation algorithms. There are seven optimisation algorithms in general, namely Artificial Bee Colony, Flower Pollination Algorithm, Gravitational Search Algorithm, Harris-Hawk Optimisation, Intelligent Firefly Algorithm, Particle Swarm Optimisation, and Teaching-Learning Based optimisation. The optimisation algorithms available do not only use the basic algorithms, but also the modified version of several parts such as randomisation terms and updating rules. Furthermore, we also provide additional options by providing more distance metrics options based on the \texttt{ rdist} package. This study showed the implementation of the \texttt{naspaclust} package in various cases, as well as comparing the performance of the algorithms in the scope of basic algorithms. The \texttt{naspaclust} package allows to improve the performance of fuzzy clustering, mainly in spatial context.

PDF arXiv

arXiv cs.CVOct 12

Building age estimation based on widely available multi-source1 geospatial data

Jialu Sui, Hongsheng Zhang, Rui Liu, Jing Ling, Ruisheng Wang +2

Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable development. Existing methods mostly rely on detailed building information such as street view, function, or materials, which are harder to obtain than geospatial data like satellite imagery and Digital Surface Models (DSMs). However, existing methods based on those geospatial data mainly rely on optical imagery and local texture features, limiting their ability to capture structural information and distinguish buildings from different periods. Moreover, the inherent temporal ordering among building age categories is often overlooked. To address these limitations, we propose the first deep-learning framework for multi-building age estimation using widely available multi-source geospatial data, including remote sensing data and their derived products, as well as building footprint information that estimates age at both pixel and instance levels within a unified pipeline. Specifically, a large pretrained backbone with fine-tuning adapters extracts representative features, and raster-derived and property-derived fusion modules support…

PDF arXiv

arXiv cs.LGOct 12

Interpretable Graph Partitioning for Spatial Prediction

Yuta Shikuri

Spatial prediction estimates unobserved values from spatially distributed observations. Although flexible predictive models are increasingly available, their practical use remains limited when interpretability is essential. In this study, we formulate a graph partitioning problem to construct a global surrogate model that approximates black-box predictions with simpler representations. Specifically, we select connected subgraphs to minimize within-cluster residuals in vertex-level predictions. Although this formulation can be viewed as a variant of spatial regionalization, it operates on model-based predictions at individual locations rather than predefined areal units. With many locations, this point-level formulation may produce artificial connections between regions, reducing interpretability. We therefore introduce a constraint that excludes such connections while retaining flexible partition boundaries. We theoretically analyze how this constraint affects the resulting partition. We also develop a computational procedure for adjusting partition boundaries. Experimental results show that the proposed method yields spatially coherent partitions with flexible boundaries.

PDF arXiv

arXiv cs.LGOct 11

Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching

Isaac Lee, Bryan Shaddy, Assad Oberai

In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flow matching. Rather than predicting spatially resolved fire progression, the proposed model forecasts a time series of fire acreage, substantially reducing computational cost and enabling efficient integration with downstream applications such as early warning systems and risk assessment tools. Given estimates of the fire size at the current and previous forecasting steps, alongside spatially averaged weather conditions, terrain characteristics, and fuel composition, the model predicts the change in fire area over a 3-hour interval. Training data are drawn from 140 coupled atmosphere-wildfire simulations generated using WRF-SFIRE, paired with weather data from the North American Mesoscale (NAM) model. Sensitivity analysis demonstrates that the model captures physically-consistent responses to variations in wind speed, temperature, and relative humidity. Model performance is evaluated against reference solutions from WRF-SFIRE. For single-step predictions, the model achieves a symmetric mean absolute percentage error (sMAPE) of 0.39…

PDF arXiv

arXiv cs.CVOct 11

Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection

Mizbaul Haque Maruf, Syed Samin Sadaf, Syed Tamzid Bakht, Md. Hasanul Kabir, Sabbir Ahmed

Traffic sign detection plays a vital role in intelligent transportation systems, supporting the safe operation of advanced driver assistance systems and autonomous vehicles. Despite significant progress in real-time object detection, accurately identifying traffic signs under challenging conditions remains difficult. Variations in lighting, adverse weather, motion blur, and partial occlusion often reduce detection performance, especially for small or visually degraded signs where important spatial information can be lost. To address these challenges, we propose Squeeze-Aware-YOLO, a lightweight traffic sign detection framework that introduces a multi-branch bottleneck module combining convolutional kernels of different sizes with a Squeeze-and-Excitation mechanism to improve feature extraction and adaptive channel weighting. In addition, we integrate the Multi-Branch Auxiliary Feature Pyramid Network and Re-parameterized Heterogeneous Multi-Scale modules to strengthen multi-scale feature representation and feature fusion while preserving real-time inference efficiency for practical deployment. The proposed approach is evaluated on the Bangladeshi Traffic Sign Detection Dataset,…

PDF arXiv

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 across biomes, limiting real-world reliability.
- FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Modelsraises · addresses
Generalist models for heterogeneous remote sensing tasks2 papers raise it · 2 address it
Specialized models for single datasets or conditions limit reuse across sensors, resolutions, and modalities.
- 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
Separate spatial or channel-wise explanations fail to capture localized, physically meaningful relevance in multispectral imagery.
- Spectral-Spatial Interpretationraises · addresses
Reproducible and benchmarkable site-specific recommendation systems1 paper raises it · 1 addresses it
Lack of versioned, loadable, and independently benchmarkable models constrains scientific reuse and validation.
- Technical Report on the Turba Fertilizer Machine Learning Stack in Moroccoraises · addresses
Data-centric pipelines for remote sensing vision-language models1 paper raises it · 1 addresses it
Lack of systematic guidance for constructing high-quality datasets and fine-tuning VLMs hinders progress in the field.
- Data-Driven Vision-Language Models for Remote Sensing: A Surveyraises · addresses

Named by an AI from these papers' abstracts.