Ten deep learning techniques to address small data problems with remote sensing

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 125, P. 103569 - 103569

Published: Nov. 18, 2023

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited training DL models, especially when these models represent key socio-environmental problems, such as the monitoring extreme, destructive climate events, biodiversity, sudden changes in ecosystem states. Such cases, also known small pose significant methodological challenges. This review summarises challenges RS domain possibility using emerging techniques to overcome them. We show that problem common challenge across disciplines scales results poor model generalisability transferability. then introduce an overview ten promising techniques: transfer learning, self-supervised semi-supervised few-shot zero-shot active weakly supervised multitask process-aware ensemble learning; we include validation technique spatial k-fold cross validation. Our particular contribution was develop flowchart helps users select which use given by answering few questions. hope our article facilitate applications tackle societally important environmental problems with reference data.

Language: Английский

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Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach

Annals of GIS, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 28

Published: May 4, 2024

The study examines the complex dynamics of changes in LULC over three decades, focused on years 1992, 2002, 2012, and 2022. research highlights significance comprehending these alterations within framework environmental socio-economic consequences. land use cover (LULC) have significant far-reaching effects ecosystems, biodiversity, human livelihoods. This offers useful information for politicians, conservationists, urban planners by examining historical patterns forecasting future changes. utilized a Multilayer Perceptron Neural Network (MLP-NN), well-known machine learning technique that excels at collecting intricate patterns. model's design had layers: input, hidden, output. model underwent 10,000 iterations during its training process, thorough statistical analysis was conducted to assess impact each driving component. MLP-NN demonstrated impressive performance, with skill measure 0.8724 an accuracy rate 89.08%. estimates 2022 verified comparing them observed data, ensuring reliability. Moreover, presence evidence likely found be factor substantial model. effectiveness accurately predicting LULC. exceptional proficiency make it powerful tool forecasts. Identifying primary causes performance understanding their implications may help enhance management strategies, encourage spatial planning, guide accurate decision-making, facilitate development policies align sustainable growth development.

Language: Английский

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A Systematic Review of Geographic Information Systems (GIS) in Agriculture for Evidence-Based Decision Making and Sustainability

Global Sustainability Research, Journal Year: 2024, Volume and Issue: 3(1), P. 1 - 24

Published: Jan. 7, 2024

The aim of this study was to consolidate current information on the utilization Geographic Information Systems (GIS) and Remote Sensing (RS) in agricultural sector, with a focus their role promoting evidence-based policies practices enhance sustainability. Additionally, review sought identify challenges hindering widespread adoption GIS RS applications, particularly low- middle-income nations. This employed methodology systematic literature review. findings indicate that technology sector has experienced notable increase over past few years. primary areas use for have been identified encompass crop yield estimation, assessment soil fertility, monitoring cropping patterns, evaluation drought conditions, detection management pests diseases, implementation precision agriculture techniques, fertilizer weed control. possesses capacity augment sustainability by incorporating spatial aspect into policies. Furthermore, potential facilitating decision making is expanding. Given escalating peril climate change food security, there exists heightened imperative include policy formulation decision-making processes practices. might be beneficial informing development effectively integrate sustainable climate-smart agriculture.

Language: Английский

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Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India

Remote Sensing Applications Society and Environment, Journal Year: 2023, Volume and Issue: 32, P. 101011 - 101011

Published: June 16, 2023

Language: Английский

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Modeling land use/land cover changes using quad hybrid machine learning model in Bangweulu wetland and surrounding areas, Zambia

Environmental Challenges, Journal Year: 2024, Volume and Issue: 14, P. 100866 - 100866

Published: Jan. 1, 2024

Wetlands are among the most productive natural ecosystems globally, providing crucial ecosystem services to people. Regrettably, a substantial 64% –71% of wetlands have been lost worldwide since 1900, mainly due changes in land use and cover (LULC). This issue is not unique Zambia's Bangweulu Wetland System (BWS), which faces similar challenges. However, there limited information about LULC BWS. Furthermore, finding accurate cost-effective methods understand dynamics complicated by multitude available techniques for classification. Non-parametric like Machine Learning (ML) offer greater accuracy, but different ML models come with distinct strengths weaknesses. Combining multiple has potential create more precise classification model. Open-source software QGIS spatial data Landsat also play significant role this endeavour. The primary objective study was enhance accuracy modeling wetland areas. Six models: Support Vector (SVM), Naive Bayes (NB), Decision Tree (DT), Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbour (KNN) were used image 8 (2020 image) 5 (1990, 2000, 2010 images) QGIS. Four SVM, NB, DT, KNN, performed better than other models. Consequently, Quad (4) hybrid model created fusing maps from these four highest performance. Results revealed that fusion classified KNN (Quad model) showcased superior performance compared individual Kappa Index scores 0.87, 0.72, 0.84 0.87 years 1990, 2020, respectively. analysis 1990 2020 showed yearly decline -1.17%, -1.01%, -0.12% forest, grassland, water body coverage, In contrast, built-up areas cropland increased at rates 1.70% 2.70%, underscores consistent growth alongside reduction forest grassland. Although experienced gradual decrease over period, minimal. Long-term monitoring will be essential evaluating success interventions, guiding conservation efforts, mitigating negative impacts on ecosystem, determining whether bodies sustained trend or short-term phenomenon.

Language: Английский

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Susceptibility of Landslide Debris Flow in Yanghe Township Based on Multi-Source Remote Sensing Information Extraction Technology (Sichuan, China)

Land, Journal Year: 2024, Volume and Issue: 13(2), P. 206 - 206

Published: Feb. 8, 2024

The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to hazards. Yanghe Township located mountainous area western Sichuan and lacks survey data. Therefore, it important predict spatial temporal development law landslide debris flow this improve effectiveness accuracy monitoring changes flow, article proposes method for extracting on flows combined with NDVI variation, which based short baseline interferometry (SBAS-InSAR) optical remote sensing interpretation. In article, we present relevant maps six main factors: vegetation index, slope, slope orientation, elevation, topographic relief, formation lithology. At same time, different images were compared sensitivity assessments. research showed that highest altitude region extracted by multi-source technology 2877 m, lowest 630 can truly reflect relief characteristics region. pixel binary model’s lack regional restrictions enables more accurate estimation Normalized Difference Vegetation Index (NDVI), bringing closer actual situation. study uncovered bidirectional relationship between coverage deformation area, revealing spatial–temporal evolution patterns. By employing technology, effectively utilized multi-period imagery feature methods depict process distribution flow. This approach not only offers technical support but also provides guidance

Language: Английский

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Assessing and predicting changes of ecosystem service values in response to land use/land cover dynamics in Ibb City, Yemen: a three-decade analysis and future outlook

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: March 11, 2024

Assessing ecosystem services values (ESV) within land use/land cover (LULC) changes is crucial for promoting human well-being and sustainable development of regional ecosystems. Yet, the spatial relationship between LULC still unclear in Yemen. This study aimed to assess impacts on ESV Ibb City, over three decades (1990–2020), predict 2050. The hybrid use classification technique classifying Landsat images, CA-Markov model prediction, benefit transfer method (BTM) assessment were employed. Our findings revealed that there was a continuous increase built-up areas barren land, with decrease cultivated grassland, which are predicted continue next 30 years. Consequently, total has decreased from US$ 68.5 × 106 1990 65.2 2020 expected further reduce 61.2 by 2050, reflecting impact urban expansion socio-economic activities ESV. provides insights future monitoring, will contribute formulation effective land-use strategies more services, particularly rapidly urbanizing data-limited regions.

Language: Английский

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Key Intelligent Pesticide Prescription Spraying Technologies for the Control of Pests, Diseases, and Weeds: A Review

Agriculture, Journal Year: 2025, Volume and Issue: 15(1), P. 81 - 81

Published: Jan. 1, 2025

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, make scientific decisions about pests, diseases, weeds; formulate personalized precision control plans; prevent pests through use of intelligent equipment. This study discusses IPSS from four perspectives: target information acquisition, processing, spraying, implementation control. acquisition section, identification based on images, remote sensing, acoustic waves, electronic nose are introduced. processing methods such as pre-processing, feature extraction, pest disease identification, bioinformatics analysis, time series data addressed. impact selection, dose calculation, time, method resulting effect formulation in a certain area explored. implement vehicle automatic technology, droplet characteristic technology their applications studied. addition, this future development prospectives IPPS technologies, including multifunctional systems, decision-support systems generative AI, sprayers. The advancement these will enhance agricultural productivity more efficient, environmentally sustainable manner.

Language: Английский

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Using hyperspectral and thermal imagery to monitor stress of Southern California plant species during the 2013–2015 drought

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 220, P. 580 - 592

Published: Jan. 16, 2025

Language: Английский

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Assessing the impact of drought and upstream dam construction on agriculture in arid and semi-arid regions: a case study of the Middle Draa Valley, Morocco

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 4, 2025

Language: Английский

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