Satellite Imagery for Deforestation Prediction using Deep Learning

Chitra, N.Thulasi; Anusha, R.; Kumar, Sheetha Hemanth; Chandana, D.; Harika, Ch.; Kumar, Vinay

doi:10.1109/iciccs51141.2021.9432087

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…To maintain the quality of the labeled data, a rigorous verification process was implemented. This process included cross-checking by multiple annotators and selective validation of segments prone to errors [4].…”

Section: ) Data Labellingmentioning

confidence: 99%

Section: ) Data Preprocessingmentioning

confidence: 99%

“…Preprocessing the satellite images was crucial to prepare the data for effective model training [4]. The preprocessing steps included normalization, where the pixel values of the images were scaled between 0 and 1, and resizing, ensuring uniform dimensions across all images [4]. These steps help maintain consistency in the input data, preventing computational inefficiencies and improving the model's performance during training [4].…”

Section: ) Data Preprocessingmentioning

confidence: 99%

“…These data pipelines were used to feed the training data in an organized manner so that model will get clean flow of, data at the time of training. This makes model much more capable to grasp subtle patterns in the data making model more accurate and precise [4]. reflectance and backscattering coefficients [4].…”

Section: ) Trainingmentioning

confidence: 99%

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Deforestation Detection Using Deep Learning Manthan Surjuse

Surjuse

2024

IJSREM

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This is a major environmental problem that affects many countries around the world” (World, 2020). In this paper, we have explored several methods of automatic method for deforestation detection such as Early Fusion Convolutional network (EFCN), Siamese Convolutional Network (S-CNN) and Support Vector Machine (SVM). As shown in all experimental results, EFCN can obviously outperform S-CNN and SVM. This work is aimed at presenting a novel curated dataset and also an approach based on deep learning, more specifically Convolutional Neural Networks (CNN) combined with cutting edge data processing techniques to solve the forestation problem. These tools, combined with more advanced deep learning models and higher resolution satellite imagery, have greatly expanded our ability to do this. Finally, this paper explains a tool for daily the detection of rainforests deforestation in satellite images from MODIS/TERRA sensor using Artificial Neural Networks and U-net architectures. “What Comes to Mind When Considering Deforestation. Image, satellite images, deep learning, and CNN (Convolutional Neural Network).”

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Section: ) Data Labellingmentioning

confidence: 99%

Section: ) Data Preprocessingmentioning

confidence: 99%

Section: ) Data Preprocessingmentioning

confidence: 99%

Section: ) Trainingmentioning

confidence: 99%

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Deforestation Detection Using Deep Learning Manthan Surjuse

Surjuse

2024

IJSREM

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“…DL algorithms for time series prediction typically require datasets made of long image sequences [10][11][12]; however, the complexity of SITS preprocessing tasks and the lack of training datasets are some limitations related to the use of DL techniques, as mentioned in [2]. Indeed, for works on the prediction of land cover classes using DL algorithms, for example, it is not easy to find datasets with SITS already preprocessed.…”

Section: Introductionmentioning

confidence: 99%

A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms

2022

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The satellite image time series are used for several applications such as predictive analysis. New techniques such as deep learning (DL) algorithms generally require long sequences of data to perform well; however, the complexity of satellite image preprocessing tasks leads to a lack of preprocessed datasets. Moreover, using conventional collection and preprocessing methods is time- and storage-consuming. In this paper, a workflow for collecting, preprocessing, and preparing Sentinel-1 images to use with DL algorithms is proposed. The process mainly consists of using scripts for collecting and preprocessing operations. The goal of this work is not only to provide the community with easily modifiable programs for image collection and batch preprocessing but also to publish a database with prepared images. The experimental results allowed the researchers to build three time series of Sentinel-1 images corresponding to three study areas, namely the Bouba Ndjida National Park, the Dja Biosphere Reserve, and the Wildlife Reserve of Togodo. A total of 628 images were processed using scripts based on the SNAP graph processing tool (GPT). In order to test the effectiveness of the proposed methodology, three DL models were trained with the Bouba Ndjida and Togodo images for the prediction of the next occurrence in a sequence.

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Deforestation rate estimation using crossbreed multilayer convolutional neural networks

Subhahan,

Kumar

2024

Multimed Tools Appl

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Satellite Imagery for Deforestation Prediction using Deep Learning

Cited by 7 publications

References 4 publications

Deforestation Detection Using Deep Learning Manthan Surjuse

Deforestation Detection Using Deep Learning Manthan Surjuse

A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms

Deforestation rate estimation using crossbreed multilayer convolutional neural networks

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