Exploiting big earth data from space - first experiences with the timescan processing chain

The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring.They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data(>250 m).However,images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions.Hence,this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of(private or public)computing clusters.The TimeScan framework covers solutions for data access to arbitrary mission archives(with different data provisioning policies)and data ingestion into a processing environment(EO2Data module),mission specific pre-processing of multi-temporal data collections(Data2TimeS module),and the generation of a final TimeScan baseline product(TimeS2Stats module)providing a spectrally and temporally harmonized representation of the observed surfaces.Technically,a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest(i.e.up to hundreds of TBs or even PBs of data)into a higher level product with significantly reduced volume.In first test,the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat–8 scenes acquired from 2013 to 2015,a global data-set of 25,550 Envisat ASAR radar images collected 2010–2012,and regional Sentinel–1 and Sentinel–2 collections of∼1500 images acquired from 2014 to 2016.The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics.

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years.The most evident implication of urbanization is the physical growth of cities.We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China.We present new spatial measures that describe‘urban land growth types’and‘fluctuations in urban land growth’within the monitoring time span with a temporal interval of five-year steps.We evaluate the correlations between urban land growth rates and socioeconomic data.Results show that(1)distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter,e.g.coastal areas exhibit the most dramatic growth rates;(2)the spatial distribution characteristics of‘urban land growth types’and‘fluctuations in urban land growth’follow similar spatial patterns across China,e.g.significant differences exist between the eastern region and other regions;and(3)a moderate correlation exists between urban area growth rate and urban population growth rate at an R2 of 0.37.By contrast,we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.

Digital world meets urban planet–new prospects for evidencebased urban studies arising from joint exploitation of big earth data,information technology and shared knowledge

The digital transformation taking place in all areas of life has led to a massive increase in digital data–in particular,related to the places where and the ways how we live.To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform(U-TEP)has been set-up.This enabling instrument represents a virtual environment that combines open access to multisource data repositories with dedicated data processing,analysis and visualisation functionalities.Moreover,it includes mechanisms for the development and sharing of technology and knowledge.After an introduction of the underlying methodical concept,this paper introduces four selected use cases that were carried out on the basis of U-TEP:two technology-driven applications implemented by users from the remote sensing and software engineering community(generation of cloud-free mosaics,processing of drone data)and two examples related to concrete use scenarios defined by planners and decision makers(data analytics related to global urbanization,monitoring of regional land-use dynamics).The experiences from U-TEP’s pre-operations phase show that the system can effectively support the derivation of new data,facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development.

Urbanization between compactness and dispersion:designing a spatial model for measuring 2D binary settlement landscape configurations

Measuring spatial patterns is a crucial task in spatial sciences.Multiple indicators have been developed to measure patterns in a quantitative manner.However,most comparative studies rely on relative comparisons,limiting their explanatory power to specific case studies.Motivated by advancements in earth observation providing unprecedented resolutions of settlement patterns,this paper suggests a measurement technique for spatial patterns to overcome the limits of relative comparisons.We design a model spanning a feature space based on two metrics-largest patch index and number of patches.The feature space is defined as‘dispersion index’and covers the entire spectrum of possible two-dimensional binary(settlement)patterns.The model configuration allows for an unambiguous ranking of each possible pattern with respect to spatial dispersion.As spatial resolutions of input data as well as selected areas of interest influence measurement results,we test dependencies within the model.Beyond,common other spatial metrics are selected for testing whether they allow unambiguous rankings.For scenarios,we apply the model to artificially generated patterns representing all possible configurations as well as to real-world settlement classifications differing in growth dynamics and patterns.

The Generalised Settlement Area:mapping the Earth surface in the vicinity of built-up areas

Geo-information on settlements from Earth Observation offers a base for objective and scalable monitoring of the evolution of cities and settlements,including their location,extent and other attributes.In this work,we deploy the best available global knowledge on the presence of human settlements and built-up structures derived from Earth Observation to advance the understanding of the human presence on Earth.We start from a concept of Generalised Settlement Area to identify the Earth surface within which any built-up structure is present.We further characterise the resulted map by using an agreement map among the state of the art of remote sensing products mapping built-up areas or other strictly related semantic abstractions as urban areas or artificial surfaces.The agreement map is formed by a grid of 1 km2,where each cell is classified according to the number of EO-derived products reporting any positive occurrence of the abstractions related to the presence of built-up structures.The paper describes the characteristics of the Generalised Settlement Area,the differences in the agreement map across geographic regions of the world,and outlines the implications for potential users of the EO-derived products used in this study.