Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap

The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there are instances where volunteer mapping is inaccurate. Despite the appeal of using OSM semantic information with remote sensing images, to train deep learning models, the crowdsourced data quality is inconsistent. High-resolution remote sensing image segmentation is a mature application in many fields, such as urban planning, updated mapping, city sensing, and others. Typically, supervised methods trained with annotated data may learn to anticipate the object location, but misclassification may occur due to noise in training data. This article combines Very High Resolution (VHR) remote sensing data with computer vision methods to deal with noisy OSM. This work deals with OSM misalignment ambiguity (positional inaccuracy) concerning satellite imagery and uses a Convolutional Neural Network (CNN) approach to detect missing buildings in OSM. We propose a translating method to align the OSM vector data with the satellite data. This strategy increases the correlation between the imagery and the building vector data to reduce the noise in OSM data. A series of experiments demonstrate that our approach plays a significant role in (1) resolving the misalignment issue, (2) instance-semantic segmentation of buildings with missing building information in OSM (never labeled or constructed in between image acquisitions), and (3) change detection mapping. The good results of precision (0.96) and recall (0.96) demonstrate the viability of high-resolution satellite imagery and OSM for building detection/change detection using a deep learning approach

«La Civiltà Cattolica» e le esposizioni torinesi (1884 e 1898)

L’articolo analizza lo sguardo della «Civiltà Cattolica», rivista dei gesuiti, sulle Esposizioni generali italiane svoltesi a Torino nel 1884 e nel 1898. In entrambe le esposizioni un ruolo centrale fu attribuito ai processi di costruzione della nazione e delle identità nazionali, all’organizzazione del consenso verso le élite dirigenti e la monarchia, attraverso un uso politico della memoria risorgimentale, oltre alla celebrazione del progresso. Nel 1884 la rivista respinse i valori laici e liberali di un’esposizione non solo tecnico-scientifica ma anche politica, rivendicando invece il contributo e la legittimità dei cattolici nelle scienze; nel 1898 la polemica, seppur presente, fu attenuata dall’affiancamento di iniziative cattoliche a quelle laiche e liberali.The paper deals with the point of view of the Jesuits’ magazine «The Catholic Civilization» about the Italian General Expositions, which took place in Turin in 1884 and 1898. In both expositions the process of nation’s construction had a central role, through a political use of the Risorgimento’s memory, in addition to the usual celebration of scientific and technological progress. In 1884 the periodical refused liberal values of the exposition, showing the significant role of the Catholics in science; in 1898 the controversy against liberals was softened thanks to the presence of both catholic and liberal events in the exposition

An explainable convolutional autoencoder model for unsupervised change detection

Abstract. Transfer learning methods reuse a deep learning model developed for a task on another task. Such methods have been remarkably successful in a wide range of image processing applications. Following the trend, few transfer learning based methods have been proposed for unsupervised multi-temporal image analysis and change detection (CD). Inspite of their success, the transfer learning based CD methods suffer from limited explainability. In this paper, we propose an explainable convolutional autoencoder model for CD. The model is trained in: 1) an unsupervised way using, as the bi-temporal images, patches extracted from the same geographic location; 2) a greedy fashion, one encoder and decoder layer pair at a time. A number of features relevant for CD is chosen from the encoder layer. To build an explainable model, only selected features from the encoder layer is retained and the rest is discarded. Following this, another encoder and decoder layer pair is added to the model in similar fashion until convergence. We further visualize the features to better interpret the learned features. We validated the proposed method on a Landsat-8 dataset obtained in Spain. Using a set of experiments, we demonstrate the explainability and effectiveness of the proposed model

Inter-Comparison of Methods for Lake Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis

Different methods are available for retrieving chlorophyll-a (Chl-a) in inland waters from optical imagery, but there is still a need for an inter-comparison among the products. Such analysis can provide insights into the method selection, integration of products, and algorithm development. This work aims at inter-comparison and consistency analyses among the Chl-a products derived from publicly available methods consisting of Case-2 Regional/Coast Colour (C2RCC), Water Color Simulator (WASI), and OC3 (3-band Ocean Color algorithm). C2RCC and WASI are physics-based processors enabling the retrieval of not only Chl-a but also total suspended matter (TSM) and colored dissolved organic matter (CDOM), whereas OC3 is a broadly used semi-empirical approach for Chl-a estimation. To pursue the inter-comparison analysis, we demonstrate the application of Sentinel-2 imagery in the context of multitemporal retrieval of constituents in some Italian lakes. The analysis is performed for different bio-optical conditions including subalpine lakes in Northern Italy (Garda, Idro, and Ledro) and a turbid lake in Central Italy (Lake Trasimeno). The Chl-a retrievals are assessed versus in situ matchups that indicate the better performance of WASI. Moreover, relative consistency analyses are performed among the products (Chl-a, TSM, and CDOM) derived from different methods. In the subalpine lakes, the results indicate a high consistency between C2RCC and WASI when a_CDOM (440) < 0.5 m^-1, whereas the retrieval of constituents, particularly Chl-a, is problematic based on C2RCC for high-CDOM cases. In the turbid Lake Trasimeno, the extreme neural network of C2RCC provided more consistent products with WASI than the normal network. OC3 overestimates the Chl-a concentration. The flexibility of WASI in the parametrization of inversion allows for the adaptation of the method for different optical conditions. The implementation of WASI requires more experience, and processing is time demanding for large lakes. This study elaborates on the pros and cons of each method, providing guidelines and criteria on their use

Characterization of the Surfaces and Near-Surface Atmospheres of Ganymede, Europa and Callisto by JUICE

We present the state of the art on the study of surfaces and tenuous atmospheres of the icy Galilean satellites Ganymede, Europa and Callisto, from past and ongoing space exploration conducted with several spacecraft to recent telescopic observations, and we show how the ESA JUICE mission plans to explore these surfaces and atmospheres in detail with its scientific payload. The surface geology of the moons is the main evidence of their evolution and reflects the internal heating provided by tidal interactions. Surface composition is the result of endogenous and exogenous processes, with the former providing valuable information about the potential composition of shallow subsurface liquid pockets, possibly connected to deeper oceans. Finally, the icy Galilean moons have tenuous atmospheres that arise from charged particle sputtering affecting their surfaces. In the case of Europa, plumes of water vapour have also been reported, whose phenomenology at present is poorly understood and requires future close exploration. In the three main sections of the article, we discuss these topics, highlighting the key scientific objectives and investigations to be achieved by JUICE. Based on a recent predicted trajectory, we also show potential coverage maps and other examples of reference measurements. The scientific discussion and observation planning presented here are the outcome of the JUICE Working Group 2 (WG2): “Surfaces and Near-surface Exospheres of the Satellites, dust and rings”