An Extensible and Easy-to-use Toolbox for Deep Learning Based Analysis of Remote Sensing Images

Maretto, Raian Vargas; Körting, Thales Sehn; Fonseca, Leila María García

doi:10.1109/igarss.2019.8898823

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In this context, according to [ 62 ], hierarchies are applied for knowledge-intensive tasks on each identified problem. Added to the discussions of [ 24 , 63 , 64 , 65 , 66 ] allied to the classification methods being divided according to the processing, into visual or digital, known as supervised, unsupervised and hybrid as per [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…In particular for matrix data, [ 22 ] defines spatial data acquisition from images from a few approaches, those being: input data definition, segmentation, detection cycles, cell space (matrix) creation, and preliminary extraction features. From the proposals [ 23 , 24 , 25 ] implemented through the GeoDMA framework (GEOBIA), in synthesis provides the realization of the steps of segmentation of satellite images, extraction of attributes, creation of classification rules, hierarchical classification and visualization of results. Additionally, the works [ 19 , 26 , 27 , 28 , 29 ] describe in detail the precautions to be taken in image acquisition and processing.…”

Section: Developmentmentioning

confidence: 99%

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Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Silva

Mendes

Blas

et al. 2022

Sensors

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Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.

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Section: Methodsmentioning

confidence: 99%

Section: Developmentmentioning

confidence: 99%

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Silva

Mendes

Blas

et al. 2022

Sensors

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“…The Deep Learning methodology presented in this paper was developed based in the baseline U-Net architecture implemented by [18], which is available in the DeepGeo package [22].…”

Section: Methodsmentioning

confidence: 99%

Exploring a Deep Convolutional Neural Network and Geobia for Automatic Recognition of Brazilian Palm Swamps (Veredas) Using Sentinel-2 Optical Data

Bendini

Fonseca

Maretto

et al. 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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The Brazilian Palm Swamps (Veredas) are a vegetation physiognomy of the Cerrado biome. It has a critical importance for biodiversity and also for groundwater sources conservation. With the irrigated agriculture intensification, it s been significantly impacted. Mapping this physiognomy is important to delimit this vegetation type to provide subsides for public policy and monitoring programs. Pixel-based methods do not succeed, since the spatial context is important for this physiognomy. Object-based methods are a great potential on this sense. Deep Learning methods, particularly the convolutional neural networks (CNN), are increasing considerably as a solution for these challenges. We applied both methods in two regions of the Cerrado and evaluated the model transferability. The results are promising, with training model overall accuracies higher than 90% for both methods. The CNN performed better when transferred a different region. We discussed some advantages and limitations, and pointed out to improvements that can still be done.

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Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest

Maretto

Fonseca

Jacobs

et al. 2021

IEEE Geosci. Remote Sensing Lett.

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An Extensible and Easy-to-use Toolbox for Deep Learning Based Analysis of Remote Sensing Images

Cited by 4 publications

References 11 publications

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Exploring a Deep Convolutional Neural Network and Geobia for Automatic Recognition of Brazilian Palm Swamps (Veredas) Using Sentinel-2 Optical Data

Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest

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