Air quality is strongly affecting human lifestyle all over the world, and its impact is apparent on healthcare, sustainable development, welfare and public administration policies. Accurate understanding of the polluting processes requires to analyze huge volumes of records, so that significant patterns and regularities can be detected. In this paper, we introduce a framework to explore the air pollution dynamics over all Europe by means of a data driven feature extraction approach. Taking advantage of MODIS records, we are able to investigate daily trends of air quality from 2003 to 2016. By means of an automatic learning scheme based on mutual information maximization, we extract the most significant patterns in the dataset. Experimental results show that the proposed approach is able to identify relevant air pollution trends that can be associated with specific physical phenomena on ground.

The recent trend toward open Earth observation (EO) data has revived a general interest in satellite-based monitoring and mapping of the Earth surface. The open policy now applied to LANDSAT data, and the starting of Sentinel operations, whose data are freely distributed even for commercial purposes, tore down a financial barrier to wider use of EO data in business activities, especially those with a narrow financial margin. Notwithstanding the flood of open data, some aspects of the Earth surface still escape satisfactory monitoring from space, especially in complex, anthropic areas. Due to insufficient spatial resolution, lack of visibility, or unsuitable revisit times, important pieces of information may not emerge from spaceborne data.In situsensing can represent a vital source of integrative information to fill the aforementioned gaps and build a more complete and accurate picture of the situation and trends in the observed area. The contribution ofin situsensing was envisaged quite early in the Earth observation history, but for a long time it remained limited to tailored sensors displaced in strategic locations. With the increasing circulation of smartphones, a new opportunity has recently opened for a different paradigm of in situ sensing, offering a huge mass of additional data by tapping on data generated by mobile devices. Even if such data may be less specialized and less usable for various reasons, the sheer size of the data flow ensures that statistical analysis will pick possible useful clues. The increasing availability of mobile connections has indeed revived the concept of “crowdsourcing,” i.e., entrusting a pool of actors with problem solution or information collection tasks. In our scenario, individuals carrying mobile devices can become “citizen sensors” on a voluntary basis by contributing data through their connected terminals. Even considering the typical issues of crowdsourced data, like quality and reliability, the balance remains definitely positive. This paper provides an overview of the theme and discusses how it relates to an important, coordinated EO initiative like Copernicus. It finally presents a specific example realized in the framework of a recent research project under the Copernicus aegis.

The concept of crowdsourcing is to collect and share information from “the crowd”. The diffusion of smartphones and tablets led to what can be pictured as a “dense network of observers” that can be used to submit useful data possibly complementing satellite-based Earth observation. In this paper, we propose a multi-purpose framework specifically designed to collect data in a distributed way; the model is based on a client-server architecture and an open-source framework including PHP and SQL. Communication among the devices is guaranteed by standard protocols like HTTP and JSON. An example of application based on this framework is also presented; the main objective is to collect information about water quality by asking users to provide feedback. The interface is both web- and mobile-based.

Satellite acquisitions from LANDSAT (LS) and CBERS programs are widely used in monitoring land cover dynamics. In the acquired products, clouds form opaque objects are obscuring parts of the scene and preventing a reliable extraction of information from these areas. Consequently, cloud shadows create similar problems, as the reflected intensity of the shadowed areas is highly reduced, generating additional info gaps. The problem can be handled by replacing clouds/shadows pixels from other close-date acquisitions, but that would assume a prior knowledge of the spatial distribution of clouds and their corresponding shadows in a scene. This research introduces a method that provides the clouds/shadows layers and their percentage in LS (TM & ETM+) and CBERS (HRCC) scenes. The approach relies on a set of literature indicators to create a composite image that enhances the visual differentiation of clouds/shadows from other objects. The created composite RGB are then warped to a relative luminance raster calculated from the linear bands components. Afterwards, the raster is processed by a K-means unsupervised classifier with a definite number of classes in order to isolate the target-layer pixels. Next, the statistical mode for the population of each class is calculated, compared and used to select the cloud/shadow class automatically, and finally the results are refined by a set of morphological filters. The processing chain avoids the usage of thresholds and highly reduces the user intervention. The achieved outcomes on various test cases are promising and stable, and encourage further developments.

Change detection is by definition the capability to detect and highlight changes occurring in space and time. Earth Observation satellites represent a fundamental source of information thanks to repeatability in time and spatial resolution. In this paper, we propose an unsupervised change detection technique capable of processing a series of single-date built-up area extractions with two main goals: determining the age of different parts of an urban area and fixing errors due to the automatic extractions suggested in previous papers by our group. Results show a general stabilization of the Kappa value but further investigation is still necessary. The proposed algorithm is available to the general public as a part of a QGIS plugin named SENSUM Earth Observation (EO) tools.

Urban expansion monitoring and organization can be performed through space-based observation thanks to the revisit time and level of details guaranteed by satellite remote sensing. In particular, the Landsat mission products are the most used thanks to the long time coverage and open access policy. This paper proposed a hybrid method - combination of pixel- and object-based analysis - in order to automatically extract built-up areas from Landsat imagery. Segments are delineated from spectral indices computed in order to increase the spectral distance among the different land cover classes. The principal component analysis is applied to the original bands and constitutes the pixel-based side of the method. Segments and PCA are then combined and classified using an unsupervised approach. Results of the method were quite satisfying with an average Kappa value over 0.5 in both case studies.

The phenomenon of urban sprawling calls for a cost-effective and rapid monitoring tool. Space borne technology offered the ability of extracting such information which is critical for urban planning and resource management. This paper introduces a newly developed indicator from Landsat acquisitions for highlighting built-up areas. On a multitemporal scale the results obtained on urban extraction were used to estimate the age of built up area, connected to codes applied in building design and ultimately to their vulnerability. In conclusion, the extracted information serves as inputs for an integrated approach on multi-risk buildings' exposure and physical vulnerability assessment and mapping.

This paper describes a combined approach used to assess damage occurred to the city of L'Aquila after the April 2009 earthquake. A survey mission in October 2011 provided the ground images of the most-affected area, still useful because of the substantially unchanged area. Main concept is the fusion of the manual-made interpretation of space-borne satellite imagery along with a ground-based damage analysis, based on the hypothesis of different damage detectability from the two considered sources. Obtained results show improvements in the total accuracy for the proposed hybrid approach confirming the initial hypothesis. This approach can be considered as an attempt to consider the crowd as a source of information (in this case information provided in the form of pictures).