Deep Learning Neural Networks for Land Use Land Cover Mapping

Storie, Christopher D.; Henry, Christopher J.

doi:10.1109/igarss.2018.8518619

Cited by 38 publications

(34 citation statements)

References 7 publications

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“…The results contained in this article were generated from this dataset. This article is an extension of the work reported by Storie and Henry (2018), and the contributions of this article are: 1) the observation that deep neural networks developed for semantic segmentation can be used to automate the task of producing LULC maps; 2) the use of FCN to produce LULC maps; and 3) a comparison of this approach to conditional random field as a recurrent neural networks (CRF-RNN) and multi-scale context aggregation by dilated convolutions (Yu and Koltun 2015). This work represents a natural application of deep learning neural networks, that were developed for performing semantic segmentation of natural colour images, to remote sensing and geoanalytics.…”

Section: Introductionmentioning

confidence: 56%

Automated LULC map production using deep neural networks

Henry

Storie

Manickam

et al. 2019

International Journal of Remote Sensing

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This article presents an approach to automating the creation of land-use/land-cover classification (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural images. This work is important since the production of accurate and timely LULC maps is becoming essential to government and private companies that rely on them for large-scale monitoring of land resource changes. In this work, deep neural networks re trained to classify each pixel of a satellite image into one of a number of LULC classes. The presented deep neural networks are all pre-trained using the ImageNet Large-Scale Visual Recognition Competition (ILSVRC) datasets and then fine-tuned using approximately 19,000 Landsat 5/7 satellite images of resolution 224 × 224 taken of the Province of Manitoba in Canada. The result is an automated solution that can produce LULC maps images significantly faster than current semi-automated methods. The contributions of this article are the observation that deep neural networks developed for semantic segmentation can be used to automate the task of producing LULC maps; the use of these networks to produce LULC maps; and a comparison of several popular semantic segmentation architectures for solving the problem of automated LULC map production.

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Section: Introductionmentioning

confidence: 56%

Automated LULC map production using deep neural networks

Henry

Storie

Manickam

et al. 2019

International Journal of Remote Sensing

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“…In [9,10], the fully convolutional network (FCN) originally developed by [8] for semantic segmentation was modified and adapted for automating the production of LULC maps. In this paper, DCNNs originally designed for image classification and object detection tasks are adapted into FCNs that take arbitrary sized input and produce image segmentations [8], i.e.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery

Alhassan

Henry

Ramanna

et al. 2019

Neural Comput & Applic

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In this article, we present an approach to Land use and Land cover (LULC) mapping from multispectral satellite images using deep learning methods. The terms satellite image classification and map production, although used interchangeably have specific meanings in the field of remote sensing. Satellite image classification describes assignment of global labels to entire scenes, whereas LULC map production involves producing maps by assigning a class to each pixel. We show that by classifying each pixel in a satellite image into a number of LULC categories we are able to successfully produce LULC maps. This process of LULC mapping is achieved using deep neural networks pre-trained on the ImageNet Large-Scale Visual Recognition Competition (ILSVRC) datasets and fine-tuned on our target dataset, which consists of Landsat 5/7 multispectral satellite images taken of the Province of Manitoba in Canada. This approach resulted in 88% global accuracy. Performance was further improved by considering the stateof-the-art generative adversarial architecture and context module integrated with the original networks. The result is an automated deep learning framework that can produce highly accurate LULC maps images significantly faster than current semi-automated methods. The contribution of this article includes extensive experimentation of different FCN architectures with extensions on a unique dataset, high classification accuracy of 90.46%, and a thorough analysis and accuracy assessment of our results.

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“…In many instances, when multiple land use classes were considered, the accuracy of the results was compromised, with values less than 0.5 being common [9]. Therefore, significant efforts were placed on developing algorithms that improve accuracy, such as [10], which obtained data for 16 classes with an accuracy of 88%, or [11], which obtained data for 10 classes with an accuracy of 98%. Increases in the accuracy of these methods are often not combined with increases in the number of classes, which would raise the utility of the results.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Crop Change in the Willamette Valley, Oregon, from 2004 to 2017

Strîmbu

Mueller‐Warrant

Trippe

2021

Data

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The Willamette Valley, bounded to the west by the Coast Range and to the east by the Cascade Mountains, is the largest river valley completely confined to Oregon. The fertile valley soils combined with a temperate, marine climate create ideal agronomic conditions for seed production. Historically, seed cropping systems in the Willamette Valley have focused on the production of grass and forage seeds. In addition to growing over two-thirds of the nation’s cool-season grass seed, cropping systems in the Willamette Valley include a diverse rotation of over 250 commodities for forage, seed, food, and cover cropping applications. Tracking the sequence of crop rotations that are grown in the Willamette Valley is paramount to answering a broad spectrum of agronomic, environmental, and economical questions. Landsat imagery covering approximately 25,303 km2 were used to identify agricultural crops in production from 2004 to 2017. The agricultural crops were distinguished by classifying images primarily acquired by three platforms: Landsat 5 (2003–2013), Landsat 7 (2003–2017), and Landsat 8 (2013–2017). Before conducting maximum likelihood remote sensing classification, the images acquired by the Landsat 7 were pre-processed to reduce the impact of the scan line corrector failure. The corrected images were subsequently used to classify 35 different land-use classes and 137 unique two-year-long sequences of 57 classes of non-urban and non-forested land-use categories from 2004 through 2014. Our final data product uses new and previously published results to classify the western Oregon landscape into 61 different land use classes, including four majority-rule-over-time super-classes and 57 regular classes of annually disturbed agricultural crops (19 classes), perennial crops (20 classes), forests (13 classes), and urban developments (5 classes). These publicly available data can be used to inform and support environmental and agricultural land-use studies.

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Deep Learning Neural Networks for Land Use Land Cover Mapping

Cited by 38 publications

References 7 publications

Automated LULC map production using deep neural networks

Automated LULC map production using deep neural networks

A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery

Agricultural Crop Change in the Willamette Valley, Oregon, from 2004 to 2017

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