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Satellite-Net: Automatic Extraction of Land Cover Indicators from Satellite Imagery by Deep Learning
Computer Vision and Pattern Recognition · Submitted 22 Jul 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In particular, the paper proposes a customized model, called Satellite-Net, able to reach an accuracy level up to 98% on test sets.
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The abstract, sentence by sentence
In this paper we address the challenge of land cover classification for satellite images via Deep Learning (DL). Land Cover aims to detect the physical characteristics of the territory and estimate the percentage of land occupied by a certain category of entities: vegetation, residential buildings, industrial areas, forest areas, rivers, lakes, etc. DL is a new paradigm for Big Data analytics and in particular for Computer Vision. The application of DL in images classification for land cover purposes has a great potential owing to the high degree of automation and computing performance. In particular, the invention of Convolution Neural Networks (CNNs) was a fundament for the advancements in this field. In [1], the Satellite Task Team of the UN Global Working Group describes the results achieved so far with respect to the use of earth observation for Official Statistics. However, in that study, CNNs have not yet been explored for automatic classification of imagery. This work investigates the usage of CNNs for the estimation of land cover indicators, providing evidence of the first promising results. In particular, the paper proposes a customized model, called Satellite-Net, able to reach an accuracy level up to 98% on test sets.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this paper we address the challenge of land cover classification for satellite images via Deep Learning (DL). | contribution 0.55 | 0.49 | 0.04 | asserted 0.85 |
| s1 | Land Cover aims to detect the physical characteristics of the territory and estimate the percentage of land occupied by a certain category of entities: vegetation, residential buildings, industrial areas, forest areas, rivers, lakes, etc. | background 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s2 | DL is a new paradigm for Big Data analytics and in particular for Computer Vision. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | The application of DL in images classification for land cover purposes has a great potential owing to the high degree of automation and computing performance. | background 1.00 | 0.07 | 0.05 | asserted 0.74 |
| s4 | In particular, the invention of Convolution Neural Networks (CNNs) was a fundament for the advancements in this field. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s5 | In [1], the Satellite Task Team of the UN Global Working Group describes the results achieved so far with respect to the use of earth observation for Official Statistics. | background 0.98 | 0.03 | 0.02 | asserted 0.98 |
| s6 | However, in that study, CNNs have not yet been explored for automatic classification of imagery. | gap 1.00 | 0.08 | 0.03 | asserted 1.00 |
| s7 | This work investigates the usage of CNNs for the estimation of land cover indicators, providing evidence of the first promising results. | contribution 0.99 | 0.88 | 0.44 | asserted 0.87 |
| s8 | In particular, the paper proposes a customized model, called Satellite-Net, able to reach an accuracy level up to 98% on test sets. | result 0.70 | 0.94 | 0.74 | asserted 0.85 |
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Original abstract
In this paper we address the challenge of land cover classification for satellite images via Deep Learning (DL). Land Cover aims to detect the physical characteristics of the territory and estimate the percentage of land occupied by a certain category of entities: vegetation, residential buildings, industrial areas, forest areas, rivers, lakes, etc. DL is a new paradigm for Big Data analytics and in particular for Computer Vision. The application of DL in images classification for land cover purposes has a great potential owing to the high degree of automation and computing performance. In particular, the invention of Convolution Neural Networks (CNNs) was a fundament for the advancements in this field. In [1], the Satellite Task Team of the UN Global Working Group describes the results achieved so far with respect to the use of earth observation for Official Statistics. However, in that study, CNNs have not yet been explored for automatic classification of imagery. This work investigates the usage of CNNs for the estimation of land cover indicators, providing evidence of the first promising results. In particular, the paper proposes a customized model, called Satellite-Net, able to reach an accuracy level up to 98% on test sets.
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