Precision Agriculture: Using Low-Cost Systems to Acquire Low-Altitude Images

Ponti, Moacir Antonelli; Chaves, Arthur A.; Jorge, Fabio R.; Costa, Gabriel B.; Colturato, Adimara Bentivoglio; Branco, Kalinka Regina Lucas Jaquie Castelo

doi:10.1109/mcg.2016.69

Cited by 33 publications

(15 citation statements)

References 9 publications

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“…Governmental programs such as ESA's Copernicus and NASA's Landsat are taking significant efforts to make such data freely available for commercial and noncommercial purpose with the intention to fuel innovation and entrepreneurship. With access to such data, applications in the domains of agriculture, disaster recovery, climate change, urban development, or environmental monitoring can be realized [37], [2], [3], [5]. However, to fully utilize the data for the previously mentioned domains, first satellite images must be processed and transformed into structured semantics [35].…”

Section: Introductionmentioning

confidence: 99%

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Helber

Bischke

Dengel

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

1,011

478

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In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.

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Section: Introductionmentioning

confidence: 99%

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Helber

Bischke

Dengel

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

1,011

478

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“…As an example, the advances in satellite technologies have increased the availability of images regularly acquired by satellite-borne sensors, while the new policies related to free availability of data (e.g., ESA Sentinel missions) support researchers to have access to massive datasets in RS. This creates the potential of DL studies for monitoring the Earth surface, e.g., for climate change analysis, urban area studies, risk and damage assessment, crop monitoring [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…This lack of large-scale publicly available benchmark datasets of RS images with multi-labels prevents the wide spread adoption of DL models in RS applications, even though raw data and potential applications do exist. To address this issue, we have recently introduced BigEarthNet [4] as a large-scale benchmark dataset for RS image understanding 1 . BigEarthNet contains 590,326 image patches, each of which is annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 (CLC 2018) [20].…”

Section: Introductionmentioning

confidence: 99%

Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

Sümbül

Charfuelàn

Demir

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

365

255

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This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. Initial research demonstrates that some CLC classes are challenging to be accurately described by considering only Sentinel-2 images. To increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models in the context of multi-label classification. Results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture, other vegetated and natural environments. All DL models are made publicly available at http://bigearth.net/#downloads, offering an important resource to guide future progress on RS image analysis. Index Terms-Sentinel-2 multispectral images, Land cover land use, Multi-label image classification, Deep neural network, Remote sensing ! arXiv:2001.06372v2 [cs.CV]

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“…A vigorous programming language and programming software (IDE) assisted with the microcontroller are proffered. [5] D. Transducers Transducers are the appliance which authorizes a physical quantity to interface with the gadgets so that it can be explicate by the operand or machine. The execution of transducers in this project is to attain appropriate functionality of the system.…”

Section: Figure: Solar Panelmentioning

confidence: 99%

An Intelligent Solar Based Farm Monitoring using Cloud Based Online Access Control Systems

B*¹,

Kumar²,

Meena³

2019

IJRTE

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Agronomy is the cornerstone of nation encountering untimely obstacles in the present scenario. Transducer nexus and their deployment in Farm Monitoring is the major utility for the nation. As agriculture is utmost provenance of nourishment, as there is a necessity to lessen the tariff and human interventions by enlarging productivity. This paper deals with advancement of IoT which lessens the endeavors of human by machine to machine interactions (corroborate through cloud based networks).The consumption of solar power by various devices can be monitored with the help of microcontroller interfaced to sensors and transmit the data through Wi-Fi devices which is stored in cloud based system. Process of irrigation is carried out in various methods using transducers which results in the distinguishing of soil moisture and temperature along with humidity conditions and it also authorizes the user to supervise the gadgets from remote locations using cloud computing systems provided with procured login credentials allocates access to know the status of all the installations through Laptop/PC/Mobile/Tablet.[1]

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Precision Agriculture: Using Low-Cost Systems to Acquire Low-Altitude Images

Cited by 33 publications

References 9 publications

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

An Intelligent Solar Based Farm Monitoring using Cloud Based Online Access Control Systems

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