Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM_2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM_2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM_2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R² of 0.69, RMSE of 5 μg/m³, and MAE of 3.3 μg/m³. The outcome of this study is a daily full coverage PM_2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM_2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.

In most parts of the electromagnetic spectrum, solar radiation cannot penetrate clouds. Therefore, cloud detection and masking are essential in image preprocessing for observing the Earth and analyzing its properties. Because clouds vary in size, shape, and structure, an accurate algorithm is required for removing them from the area of interest. This task is usually more challenging over bright surfaces such as exposed sunny deserts or snow than over water bodies or vegetated surfaces. The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VEN$\mu$S satellite images. To fulfil this goal, three VEN$\mu$S tiles in Israel were selected. The tiles represent different land-use and cover categories, including vegetated, urban, agricultural, and arid areas, as well as water bodies, with a special focus on bright desert surfaces. Additionally, the study examines the effect of various channel inputs, exploring possibilities of broader usage of these architectures for different data sources. It was found that among the tested architectures, U-Net performs the best in most settings. Its results on a simple RGB-based dataset indicate its potential value for any satellite system screening, at least in the visible spectrum. It is concluded that all of the tested architectures outperform the current VEN$\mu$S cloud-masking algorithm by lowering the false positive detection ratio by tens of percents, and should be considered an alternative by any user dealing with cloud-corrupted scenes.

Any place the human species inhabits is inevitably modified by them. One of the first features that appear everywhere, in urban areas as well as in the countryside or deep forests, are roads. Further, roads and streets in general reflect their omnipresent and significant role in our lives through the flow of goods, people, and even culture and information. However, their contribution to the public is highly influenced by their surface. Yet, research on automated road surface classification from remotely sensed data is peculiarly scarce. This work investigates the capacities of chosen convolutional neural networks (fully convolutional network (FCN), U-Net, SegNet, DeepLabv3+) on this task. We find that convolutional neural network (CNN) are capable of distinguishing between compact (asphalt, concrete) and modular (paving stones, tiles) surfaces for both roads and sidewalks on aerial data of spatial resolution of 10 cm. U-Net proved its position as the best-performing model among the tested ones, reaching an overall accuracy of nearly 92%. Furthermore, we explore the influence of adding a near-infrared band to the basic red green blue (RGB) scenes and stress where it should be used and where avoided. Overfitting strategies such as dropout and data augmentation undergo the same examination and clearly show their pros and cons. Convolutional neural networks are also compared to single-pixel based random forests and show indisputable advantage of the context awareness in convolutional neural networks, U-Net reaching almost 25% higher accuracy than random forests. We conclude that convolutional neural networks and U-Net in particular should be considered as suitable approaches for automated semantic segmentation of road surfaces on aerial imagery, while common overfitting strategies should only be used under particular conditions.

The use of in situ references in Earth observation monitoring is a fundamental need. LUCAS (Land Use and Coverage Area frame Survey) is an activity that has performed repeated in situ surveys over Europe every three years since 2006. The dataset is unique in many aspects; however it is currently not available through a standardized interface, machine-to-machine. Moreover, the evolution of the surveys limits the performance of change analysis using the dataset. Our objective was to develop an open-source system to fill these gaps. This paper presents a developed system solution for the LUCAS in situ data harmonization and distribution. We have designed a multi-layer client-server system that may be integrated into end-to-end workflows. It provides data through an OGC (Open Geospatial Consortium) compliant interface. Moreover, a geospatial user may integrate the data through a Python API (Application Programming Interface) to ease the use in workflows with spatial, temporal, attribute, and thematic filters. Furthermore, we have implemented a QGIS plugin to retrieve the spatial and temporal subsets of the data interactively. In addition, the Python API includes methods for managing thematic information. The system provides enhanced functionality which is demonstrated in two use cases.