EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
In this paper, we address the challenge of land use and land cover classification using remote sensing satellite images. For this challenging task, we use the openly and freely accessible Sentinel-2 satellite images provided within the scope of the Earth observation program Copernicus. The key contributions are as follows. We present a novel dataset based on satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Network (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards a number of Earth observation applications. We demonstrate how the classification system can be used for detecting land use or land cover changes and how it can assist in improving geographical maps.
We are currently at the edge of having public and continuous access to satellite image data for Earth observation. Governmental programs such as ESA’s Copernicus and NASA’s Landsat are taking significant efforts to make such data freely available for commercial and non-commercial 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 [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. One type of such fundamental semantics is Land Use and Land Cover Classification [1, 29]. The aim of land use and land cover classification is to automatically provide labels describing the represented physical land type or how a land area is used (e.g., residential, industrial).
As often in supervised machine learning, performance of classification systems strongly depends on the availability of high-quality datasets with a suitable set of classes . In particular when considering the recent success of deep Convolutional Neural Networks (CNN) , it is crucial to have large quantities of training data available to train such a network. Unfortunately, current land use and land cover datasets are small-scale or rely on data sources which do not allow the mentioned domain applications.
In this paper, we propose a novel satellite image dataset for the task of land use and land cover classification. The proposed EuroSAT dataset consists of 27,000 labeled images with a total of 10 different classes. A significant difference to previous datasets is that the presented satellite image dataset covers 13 spectral bands allowing to investigate multi-modal fusion approaches in the context of these bands. This is in particular an open challenge when considering deep neural networks for classification. In addition, the proposed dataset is based on openly and freely accessible Earth observation data allowing a unique range of applications. The labeled dataset EuroSAT is made publicly available
Further, we provide a full benchmark by using deep CNNs on the dataset demonstrating robust classification performance which can be taken as foundation to develop applications for the previously mentioned domains. We outline how the classification model can be used for detecting land use or land cover changes and how it can assist in improving geographical maps.
We provide this work in the context of the recently published EuroSAT dataset, which can be used similar to  as a basis for large-scale training of deep learning networks for satellite images.
2 Related Work
In this section, we review previous research in the area of land use and land cover classification. In this context, we present datasets consisting of airborne images as well as datasets consisting of satellite images. Furthermore, we present state-of-the-art methods in land use and land cover classification.
2.1 Classification Datasets
Remote sensing image classification is a challenging task. The progress of classification in the remote sensing area has particularly been inhibited due to the lack of reliably labeled ground truth datasets. A popular and intensively researched [6, 19, 20, 27, 29] remote sensing image classification dataset known as UC Merced Land Use Dataset (UCM) was introduced by Yang et al. . The dataset consists of 21 land use and land cover classes. Each class has 100 images and the contained images measure 256x256 pixels with a spatial resolution of about 30 cm per pixel. All images are in the RGB color space and were extracted from the USGS National Map Urban Area Imagery collection, i.e. the underlying images were acquired from an aircraft. Unfortunately, a dataset with 100 images per class is very small-scale. Trying to enhance the dataset situation, various researchers used non-freely usable Google Earth
Research closer to our work was provided by Penatti et al.  and Basu et al. . Penatti et al. investigated remote sensing satellite images having a spatial resolution of 10 meters per pixel. Based on these images,  introduced the Brazillian Coffee Scene dataset (BCS). The dataset covers the two classes coffee crop and non-coffee crop. Each class consists of 1,423 images. The images consist of a red, green and near-infrared band. Basu et al.  introduced the SAT-6 dataset relying on aerial images. This dataset has been extracted from images with a spatial resolution of 1 meter per pixel. The image patches are created by using images from the National Agriculture Imagery Program (NAIP). SAT-6 covers the 6 different classes: barren land, trees, grassland, roads, buildings and water bodies. The proposed patches have a size of 28x28 pixels per image and consist of a red, green, blue and a near-infrared band.
2.2 Land Use and Land Cover Classification
While CNNs are an established classification method , primarily with the impressive results on image classification challenges [12, 21, 23], deep CNNs became a common and popular image classification method. In remote sensing image classification, various different feature extraction and classification methods (e.g., Random Forest) were evaluated on the introduced datasets. Yang et al. evaluated bag-of-visual-words and spatial extension approaches on the UCM dataset . Basu et al. investigated deep belief networks, basic CNNs and stacked denoising autoencoders on the SAT-6 dataset . Basu et al. also represented an own framework for the land cover classes introduced in the SAT-6 dataset which extracts features from input images, normalizes the extracted features and used the normalized features as input to a deep belief network. Besides low-level color descriptors, Penatti et al. evaluated deep CNNs on the UCM and BCS dataset . In addition to deep CNNs, Castelluccio et al. intensively evaluated various methods (e.g., bag-of-visual-words, spatial pyramid match kernel) for the classification of the UCM and BCS dataset. In the context of deep learning, the used deep CNNs have been trained from scratch or fine-tuned by using a pretrained network [6, 19, 7, 16]. Mainly, the networks were pretrained on the dataset from the ILSVRC-2012 image classification challenge . Thus, these pretrained networks were trained on images from a totally different domain. However, the features generalized well and therefore these pretrained networks proved to be suitable for remote sensing image classification . The examined works show that deep CNNs outperform all previous state-of-the-art approaches on the introduced datasets [6, 17, 15, 27].
3 Dataset Acquisition
Besides NASA with its Landsat Mission
Sentinel-2A is one out of a two-satellite constellation consisting of the identical land monitoring satellites Sentinel-2A and Sentinel-2B. The satellites were successfully launched in June 2015 and March 2017. Both sun-synchronous satellites capture the global Earth’s land surface with a Multispectral Imager (MSI) covering 13 different spectral bands listed in Table 1. The three bands B01, B09 and B10 are intended to be used for the correction of atmospheric effects (e.g., aerosols, cirrus or water vapor). The remaining bands are primarily intended to identify and monitor land use and land vegetation. In addition to mainland, large islands as well as inland and coastal waters are covered by these two satellites. Each satellite will deliver optical imagery for at least 7 years with a spatial resolution of up to 10 meters per pixel. Both satellites carry fuel for up to 12 years of operation which allows for an extension of the operation. The two-satellite constellation generates a coverage of almost the entire Earth’s land surface every five days, i.e. the satellites scan each point in the covered area every five days.
In order to show the spatial resolution of 10 meters per pixel, Figure 4 illustrates a sample scene originated from the combination of the red (B04), green (B03) and blue (B02) band. To emphasize different optical aspects, several spectral bands can be combined. In remote sensing, these combinations are used to make different aspects visible, which are poorly visible or cannot be seen using RGB color space images. One example is the combination of shortwave-infrared, near-infrared and green light to identify floods. An example of a color-infrared image, which results from the combination of the near-infrared (B08), red (B04) and green (B03) band, is shown in Figure 3. A shortwave-infrared image arisen from the combination of the shortwave-infrared 2 (B12), red edge 4 (B08A) and red (B04) band is also shown in Figure 3. Furthermore, Figure 3 shows an image consisting of merely the near-infrared (B08) band.
We are convinced that the vast data volume of these satellies in combination with powerful machine learning methods will influence future research. Therefore, one of our key research aims is to make this vast data source accessible for machine learning applications. To construct an image classification dataset, we conducted the following steps:
Satellite Image Acquisition: We gathered satellite images of cities distributed in over 30 European countries.
Dataset Creation: Based on the obtained satellite images, we created a dataset of 27,000 labeled image patches. The image patches measure 64x64 pixels and have been manually checked.
3.1 Satellite Image Acquisition
We have downloaded satellite images recorded by the satellite Sentinel-2A via Amazon S3
We aimed for the objective to comprise as many countries as possible in order to cover the high intra-class variance inherent to remote sensing image classes. Furthermore, we have extracted images recorded all over the year to get variance as high as possible inherent in the covered land use and land cover classes.
|B01 - Aerosols||60||443|
|B02 - Blue||10||490|
|B03 - Green||10||560|
|B04 - Red||10||665|
|B05 - Red edge 1||20||705|
|B06 - Red edge 2||20||740|
|B07 - Red edge 3||20||783|
|B08 - NIR||10||842|
|B08A - Red edge 4||20||865|
|B09 - Water vapor||60||945|
|B10 - Cirrus||60||1375|
|B11 - SWIR 1||20||1610|
|B12 - SWIR 2||20||2190|
3.2 Dataset Creation
The amount of available satellite data is tremendous (the two-satellite constellation provides about 1.6 TB of compressed images per day). Unfortunately, even with this amount of data, supervised machine learning is constrained due to the lack of ground truth data. Motivated by the observation that existing benchmark datasets are not satisfying for the intended applications with Sentinel-2 satellite images and the objective to make this open and free satellite data accessible to various Earth observation applications, we generated a labeled dataset. The dataset consists of 10 different classes with 2,000 to 3,000 images per class. In total, the dataset has 27,000 images. The patches measure 64x64 pixels. We have chosen 10 different land use and land cover classes based on the principle that they showed to be visible at the resolution of 10 meters per pixel and are frequently enough covered by the European Urban Atlas to generate thousands of image patches. To differentiate between different agricultural land uses, the proposed dataset covers the classes annual crop, permanent crop (e.g., fruit orchards, vineyards or olive groves) and pastures. The dataset also discriminates built-up areas. It therefore covers the classes highway, residential and industrial areas. Different water bodies appear in the classes river and sea & lake. Furthermore, undeveloped environments such as forest and herbaceous vegetation are comprised. An overview of the covered classes with four samples per class is illustrated in Figure 15.
We manually checked all 27,000 images multiple times and corrected the ground truth by sorting out mislabeled images as well as images full of snow/ice. Example images which have been discarded are shown in Figure 16. The samples are intended to show industrial areas. Clearly, no industrial area is visible. Please note, the proposed dataset has not received atmospheric correction. This can result in images with a color cast. Extreme cases are visualized in Figure 17. With the intention to advocate the classifier to also learn these cases, we did not filter the respective samples and let them flow into the dataset.
Besides the 13 covered spectral bands, the new dataset has two central innovations. Firstly, the dataset is not based on non-free satellite images like Google Earth imagery or rely on old remote sensing data which is not available on a high-frequent basis in future (e.g., NAIP used in ). Instead, an open and free Earth observation program whose satellites deliver images for the next 7 to 12 years is used allowing real-world Earth observation applications
4 Dataset Benchmarking
As shown in previous work [6, 15, 17, 19], deep CNNs have demonstrated to outperform all previous approaches on land use and land cover classification datasets. Accordingly, we use the state-of-the-art deep CNN models GoogleNet  and ResNet-50 [9, 10] for the classification of the introduced datasets. These networks evolved by innovations such as the inception module [25, 26, 24, 14] and the residual unit [9, 10].
For the proposed EuroSAT dataset, we also investigate the performance of the 13 spectral bands with respect to the classification task. In this context, the classification performance using single-band images as well as images based on common band combinations are evaluated.
4.1 Comparative Evaluation
In order to train and evaluate deep CNNs on the proposed novel and the introduced existing datasets, we respectively split the data for each dataset into a training and test set at the ratio of 80:20. We ensured that the split is applied class-wise. While the red, green and blue bands are covered by almost all airborne or satellite image classification datasets, the proposed EuroSAT dataset consists of 13 spectral bands. For the comparative evaluation, we computed images in the RGB color space by combining the bands red (B04), green (B03) and blue (B02). For the respective dataset, we fine-tuned GoogLeNet and ResNet-50 CNN models, which were pretrained on the ILSVRC-2012 image classification dataset. . In all evaluations, we first trained the last layer with a learning rate of 0.01. Afterwards, we fine-tuned through the entire network with a low learning rate between 0,001 and 0,0001.
We computed the overall classification accuracy to evaluate the performance of the different CNN models on the investigated datasets. Table 2 lists the achieved classification results for the different CNN models. The deep CNNs achieve state-of-the-art results on the UCM dataset and outperform previous results on the other three presented datasets by about 2-4% (AID, SAT-6, BCS) [6, 19, 22]. Table 2 shows that the ResNet-50 architecture performs best on the introduced EuroSAT land use and land cover classes. In order to allow an evaluation on the class level, Figure 18 shows the confusion matrix of this best performing network. Even if rarely, it is shown that the classifier sometimes confuses the agricultural land classes as well as the classes highway and river.
4.2 Band Evaluation
In order to investigate the performance of deep CNNs using single-band images as well the common shortwave-infrared and color-infrared band combinations, we used the pretrained ResNet-50 with a fixed training-test-split to compare the performance of the different spectral bands. For the single-band image evaluation, we used images as input consisting of the information gathered from a single spectral band. We investigated all spectral bands, even the bands not intended for land monitoring. Bands with a lower spatial resolution have been upsampled to 10 meters per pixel using cubic-spline interpolation . Figure 19 shows a comparison of the spectral band’s performance. It is shown that the red, green and blue bands outperform all other bands. Interestingly, the bands red edge 1 (B05) and shortwave-infrared 2 (B12) with an original spatial resolution of merely 20 meters per pixel showed an impressive performance. The two bands even outperform the near-infrared band (B08) which has a spatial resolution of 10 meters per pixel.
|Band Combination||Accuracy (ResNet-50)|
In addition to the RGB band combination, we also investigated the performance of the shortwave-infrared and color-infrared band combinations. Table 3 shows a comparison of the performance of these combinations. As shown, image combinations outperform single-band images. Furthermore, images in the RGB color space performed best on the introduced land use and land cover classes.
Please note, networks pretrained on the ILSVRC-2012 image classification dataset have initially not been trained on images other than RGB images.
The openly and freely accessible satellite images offer a broad range of possible applications. In this section, we demonstrate that the novel dataset published with this paper allows going beyond the pure scientific classification but also delivers impact for real-world applications. The classification result with an overall accuracy of 98.57% paves the way for these applications. We show applications in the area of land use and land cover change detection as well as how the proposed research can assist in keeping geographical maps up-to-date.
5.1 Land Use and Land Cover Change Detection
Since the two-satellite constellation will scan the Earth’s land surface for about the next decade on a repeat cycle of five days, a trained classifier can be used for monitoring land surfaces and detect changes in land use or land cover.
To demonstrate land use and land cover change detection, we selected images from the same spatial region but from different points in time. Using the trained classifier, we investigated 64x64 image regions. A change has taken place if the classifier delivers different classification results on patches taken from the same spatial 64x64 region. In the following, we show three examples of spotted changes. In the first example shown in Figure 20, the classification system recognized that in the marked area the land has changed. The left image was acquired in the surroundings of Shanghai, China in December 2015 showing an area classified as industrial. The right image was acquired in the same area in December 2016 showing that the industrial buildings have been demolished. The second example is illustrated in Figure 21. The left image was acquired in the surroundings of Dallas, USA in August 2015 showing no dominant residential area in the highlighted area. The right image was acquired in the same area in March 2017. The system has identified a change in the highlighted area showing a residential area has been constructed. The third example presented in Figure 22 shows that the system detected deforestation near Villamontes, Bolivia. The left image was acquired in October 2015. The right image was acquired in September 2016 showing that a large area has been deforested.
The shown examples find their usage in urban area development, nature protection or sustainable development. For instance, since deforestation is a main contributor to climate change, the detection of deforestation is particularly of interest (e.g., to notice illegal clearing of forest).
5.2 Assistance in Mapping
While a classification system using 64x64 patches does not allow a finely graduated segmentation or mapping, it cannot only detect changes as shown in the previous examples, it can also facilitate keeping maps up-to-date. This foundation is an extremely helpful assistance with maps created in a crowdsourced manner like OpenStreetMap (OSM). A possible system could verify already tagged areas, identify mistagged areas or bring large area tagging. As shown in Figure 23, the industrial areas seen in the left up-to-date satellite image are almost completely covered in the corresponding OSM mapping. The right up-to-date satellite image also shows industrial areas. However, a major part of the industrial areas is not covered in the corresponding map. Due to the high temporal availability of Sentinel-2 satellite images in the future, the proposed research together with the published dataset can be used to build systems which assist in keeping maps up-to-date. A detailed analysis of the respective land area can then be provided using high-resolution satellite images and an advanced segmentation approach [4, 11].
In this paper, we have addressed the challenge of land use and land cover classification. For this task, we presented a novel dataset based on remote sensing satellite images. To obtain this dataset, we have used the openly and freely accessible Sentinel-2 images provided within the scope of the Earth observation program Copernicus. The proposed dataset consists of 10 classes covering 13 different spectral bands with in total 27,000 labeled images. We evaluated state-of-the-art deep CNNs on this novel dataset. We also evaluated deep CNNs on existing remote sensing datasets and compared the obtained results. For the novel dataset, we investigated the performance based on different spectral bands. As a result of this evaluation, the RGB band combination outperformed all single-band images as well as the shortwave-infrared and the color-infrared band combination with an overall classification accuracy of 98.57%. For the existing datasets, we achieved state-of-the-art and outperforming results. Overall, the available free Sentinel-2 satellite images offer a broad choice of possible applications. The proposed research is a first important step to make use of the vast available satellite data in machine learning allowing classification and monitoring of Earth’s land surfaces in future on a large scale. The proposed research can be leveraged for multiple real-world Earth observation applications. Possible applications in the area of land use and land cover change detection and improving geographical maps have been shown.
This work was partially funded by the BMBF project Multimedia Opinion Mining (MOM: 01WI15002). The authors would like to thank NVIDIA for the support within the NVIDIA AI Lab program.
- The one-satellite constellation has a repeat cycle of 10 days.
- The satellites Sentinel-2C and Sentinel-2D are already planned to continue the Earth observation mission afterwards.
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