Crop classification from Sentinel-2 time series with temporal convolutional neural networks

Automated crop identification tools are of interest to a wide range of applications related to the environment and agriculture including the monitoring of related policies such as the European Common Agriculture Policy. In this context, this work presents a parcel-based crop classification system which leverages on 1D convolutional neural network supervised learning capacity. For the training and evaluation of the model, we employ open and free data: (i) time series of Sentinel-2 optical data selected to cover the crop season of one year, and (ii) a cadastre-derived database providing detailed delineation of parcels. By considering the most dominant crop types and the temporal features of the optical data, the proposed lightweight approach discriminates a considerable number of crops with high accuracy

Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen

Towards a Map of the European Tree Cover based on Sentinel-2

Many areas of science and policy depend on knowledge of the tree cover in Europe. Sentinel-2 is a new (launched in 2015) satellite with a higher spatial resolution compared to previous satellites. In the present study a new algorithm for mapping tree cover from Sentinel-2 is developed, an analysis of which bands should be used for tree cover mapping is made, the accuracy of the mapping is assessed, and the tree cover from the present approach is compared with previous estimates. Firstly, the feasibility of the present algorithm is demonstrated. Secondly, it is shown that only ten band combinations have good performance in four selected Sentinel-2 tiles and that the bands 3, 5, 6, 12 appear in most combinations. Thirdly, the accuracy is assessed to be high, and lastly it is shown that the relative difference between the tree cover of the present study and the tree cover of previous studies is between -14% and 68

A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

Editorial of Special Issue “Machine and Deep Learning for Earth Observation Data Analysis”

Earth observation and remote sensing technologies provide ample and comprehensive information regarding the dynamics and complexity of the Earth system [...

On the Assessment of Automatically Processing HR/VHR Imagery Using Low-Resolution Global Reference Data

Recently, we presented a general framework for processing high and very high resolution images (HR/VHR) with the prospect of automatically constructing a Global Human Settlement Layer (GHSL) [1]. This work constitutes an integral part of this effort and focuses on finding fairly reliable modes for the assessment of the produced image information. We describe the validation process together with the performance measures and we apply regression analysis in order to investigate the relationship, if any, among the results derived from human interpretation, the outcome of our methodology and established global reference data as MODIS 500-m urban.JRC.G.2-Global security and crisis managemen

Incremental machine learning methods in time-dependent problems: pattern, time-series and system recognition applications in real-time decision-making

The thesis proposes pattern recognition techniques based on machine learning methods and automated data mining; the application domain is systems that evolve over time. It describes algorithms and resolution strategies that combine and exploit features from established methodologies developed in the framework of computational and statistical learning. The attributes of the proposed identification and recognition techniques are: 1) low time-space complexity, 2) incremental and fast learning, 3) real-time response, 4) application independence, 5) simplicity, 6) automated operation, 7) scalability, and 8) distributed computing. After the application of these techniques, the produced solutions are not proved to be the optimal ones; nevertheless they are sufficient and functional. The empirical verification is implemented in real-world problems of different complexity. These problems can be represented by time evolving systems and emerge from the fields of environmental parameters forecasting, voice/speech and image recognition, and video-based content extraction such as human pose recovery and human action recognition. It is known that time evolution occurs in a variety of problems encountered in physics, chemistry, biology, economy and more; thus, the proposed recognition schemes present a wide range of applications. Additionally, the main trait of incremental learning algorithms is the integration of new information into the knowledge base without the need for the model’s retraining. This attribute is applicable to domains where the data flow is continuous such as autonomous robots learning, information retrieval on the Web, human-machine interaction, medical monitoring devices, error diagnosis, security systems, predictive models for natural phenomena and more. The difficulty in defining the analytical form of functions that describe the aforementioned problems, the high dimensionality of data sets, the intervals of intense discontinuity and the presence of high noise make impossible the application of the differential calculus and classical linear or nonlinear analysis. This research work demonstrates experimentally that the adoption of techniques which combine the regression with continuous clustering and classification, allows the handling of non- well defined problems, especially in those cases where the conditions that lead to the convergence to the optimal solution cannot be verified in real world.Η παρούσα διατριβή προτείνει τεχνικές αναγνώρισης προτύπων/μορφωμάτων που βασίζονται σε μεθόδους μηχανικής μάθησης και αυτόματης εξόρυξης δεδομένων, με αντικείμενο εφαρμογής τα συστήματα που εξελίσσονται στο χρόνο. Περιγράφονται αλγόριθμοι και στρατηγικές επίλυσης που συνδυάζουν και αξιοποιούν χαρακτηριστικά από καθιερωμένες μεθοδολογίες που αναπτύχθηκαν στα πλαίσια της υπολογιστικής και στατιστικής μάθησης. Οι ιδιότητες με τις οποίες εφοδιάζονται οι προτεινόμενες τεχνικές αναγνώρισης είναι: 1) χαμηλή χωρο-χρονική πολυπλοκότητα, 2) σταδιακή και ταχεία μάθηση, 3) απόκριση σε πραγματικό χρόνο, 4) ανεξαρτησία από το συγκεκριμένο πεδίο εφαρμογής τους, 5) απλότητα στη χρήση, 6) αυτοματοποιημένη λειτουργία, 7) επεκτασιμότητα, και 8) δυνατότητα κατανεμημένης επεξεργασίας. Οι λύσεις που προκύπτουν μετά την εφαρμογή των εν λόγω τεχνικών δε βεβαιώνεται ότι είναι οι βέλτιστες, ωστόσο είναι επαρκείς και λειτουργικές. Η εμπειρική επαλήθευση υλοποιείται σε διαφορετικής πολυπλοκότητας προβλήματα του πραγματικού κόσμου, τα οποία δύναται ν' αναπαρασταθούν από χρονικώς εξελισσόμενα συστήματα. Τα προβλήματα που αντιμετωπίστηκαν προέρχονται από το χώρο της πρόβλεψης περιβαλλοντικών παραμέτρων, από τους χώρους αναγνώρισης φωνής/ομιλίας και εικόνας, καθώς και από το πεδίο αναγνώρισης περιεχομένου μέσω οπτικών εγγραφών (video), όπως είναι η ανάκτηση του ανθρώπινου σώματος και η αναγνώριση ανθρώπινης δραστηριότητας. Όπως είναι γνωστό, η χρονική εξέλιξη συναντάται σε μια πληθώρα προβλημάτων που εμφανίζονται στη φυσική, στη χημεία, στη βιολογία, στην οικονομία κα., με αποτέλεσμα οι διατάξεις αναγνώρισης που προτείνονται να παρουσιάζουν ένα ευρύ πεδίο εφαρμογών. Επιπρόσθετα, το κύριο χαρακτηριστικό των αλγορίθμων προοδευτικής μάθησης και αναγνώρισης είναι η ενσωμάτωση καινούργιας πληροφορίας στην υπάρχουσα γνωσιακή δεξαμενή επεξεργασμένων δεδομένων όποτε αυτή είναι διαθέσιμη, αποφεύγοντας έτσι το σκόπελο της επανεκπαίδευσης. Αυτή η ιδιότητα βρίσκει εφαρμογή σε προβλήματα όπου υπάρχει συνεχής ροή πληροφορίας, όπως στην εκμάθηση των αυτόνομων ρομπότ, στην ανάκτηση πληροφοριών στον Παγκόσμιο Ιστό, στην αλληλεπίδραση ανθρώπου-μηχανής, σε συσκευές ιατρικής παρακολούθησης, στη διάγνωση σφαλμάτων, σε συστήματα ασφαλείας, σε προγνωστικά μοντέλα φυσικών φαινομένων κα. Η δυσκολία ορισμού της αναλυτικής μορφής των συναρτήσεων που περιγράφουν τα εν λόγω προβλήματα, η υψηλή διαστατικότητα των συνόλων δεδομένων, τα διαστήματα έντονης ασυνέχειας, καθώς και η ύπαρξη υψηλού θορύβου καθιστούν ανέφικτη την εφαρμογή του διαφορικού λογισμού και της κλασικής γραμμικής ή μη-γραμμικής ανάλυσης. Στην παρούσα ερευνητική εργασία αποδεικνύεται πειραματικά ότι η υιοθέτηση τεχνικών που συνδυάζουν την παλινδρόμηση με τη συνεχή ομαδοποίηση και ταξινόμηση, επιτρέπει το χειρισμό μη-καλώς ορισμένων προβλημάτων, ειδικά εκείνων των περιπτώσεων όπου οι συνθήκες που διαμορφώνουν τη σύγκλιση στη βέλτιστη λύση δεν μπορούν να επαληθευτούν στο φυσικό κόσμο

A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning

This work introduces a new classification method in the remote sensing domain, suitably adapted to dealing with the challenges posed by the big data processing and analytics framework. The method is based on symbolic learning techniques, and it is designed to work in complex and information-abundant environments, where relationships among different data layers are assessed in model-free and computationally-effective modalities. The two main stages of the method are the data reduction-sequencing and the association analysis. The former refers to data representation; the latter searches for systematic relationships between data instances derived from images and spatial information encoded in supervisory signals. Subsequently, a new measure named the evidence-based normalized differential index, inspired by the probability-based family of objective interestingness measures, evaluates these associations. Additional information about the computational complexity of the classification algorithm and some critical remarks are briefly introduced. An application of land cover mapping where the input image features are morphological and radiometric descriptors demonstrates the capacity of the method; in this instructive application, a subset of eight classes from the Corine Land Cover is used as the reference source to guide the training phase