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News – MapTailor Geospatial Consulting

News

MapTailor wins EARSC Best Start-Up Company Award 2019

We have some exciting news that we would like to share we you. MapTailor was the proud recipient of this years’ Earth Observation Startup Award by the European Association of Remote Sensing Companies (EARSC) during the EARSC annual assembly in Brussels. As a start-up that prides itself in working collaboratively and in close partnership with…
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Follow us on GitHub

We started sharing codes and functions written in R and Python on our GitHub account. In the near future, we will upload codes that can be used for – among others – pre-processing of Sentinel data, supervised image classification, and many more. Have a look at https://github.com/MapTailor and follow us.

New Special Issue About Index Insurance Online

Together with our colleagues from the International Research Institute for Climate and Society, and the University of Natural Resources and Life Sciences (BOKU) we have a new special issue on „Earth Observation for Index Insurance“ online at MPDI Remote Sensing. This Special Issue aims to (i) present and showcase the latest advances in remote sensing science;…
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New Special Issue About Land Abandonment Online

Together with our colleagues from University of Copenhagen, and the Leibniz Institute of Agricultural Development in Transition Economies (IAMO) we have a new special issue on „Agricultural Land Abandonment: Patterns, Drivers and Consequences“ online at MPDI Land. This Special Issue on „Agricultural Land Abandonment: Patterns, Drivers and Consequences“ is dedicated to bringing advances on our…
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New publication about satellite based crop identification

We are glad to announce the new publication about „A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics“ of our colleagues from the Center for Remote Sensing of Land Surfaces, ZFL (Bonn, Germany). Accurate classification and mapping of crops is essential for supporting sustainable land management. Such maps can be created based…
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MapTailor@IGARSS 2018

We are glad to co-author a new publication titled ‚Object-based post-processing method for crop classification“ of our colleagues from the Center for Remote Sensing of Land Surfaces (ZFL) in Bonn (Germany), and the Space Research Institute NASU-SSAU (Ukraine). In this paper, we propose a novel method for an object-based post-classification filtering, specifically tailored to improve…
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Partner in ESA project „EO-li“

MapTailor is partner of the project EO-li (Earth Observation based location identifier platform). The project lead is with mundialis GmbH & Co. KG in Bonn, Germany. EO-li – supported by the European Space Agency (ESA) – is a service platform which tackles the question of restoration in its large sens and its positive spin offs…
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We welcome our new remote sensing consultant

We welcome our new remote sensing consultant at MapTailor. Dr. Leonhard Suchenwirth is a geographer with more than 10 years experience in GIS and remote sensing (technologies/research/applications). Besides research, he has worked in public servies, commercial and consulting enterprises on local, national and international level.His projects range from agricultural monitoring, forestry and carbon budgeting, water…
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Special Issue about Agricultural Monitoring in MPDI

Together with our colleagues from University of Copenhagen, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), and University of Natural Resources and Life Sciences (BOKU) we have a special issue on „Monitoring Agricultural Land-Use Change and Land-Use Intensity“ online at MPDI Remote Sensing. This Special Issue will draw from ongoing advancements and novel developments…
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New publication about cropland classification

We are glad to announce the new publication about „The impact of training class proportions on binary cropland classification“ of our colleagues from the Earth and Life Institute, Environmental Sciences, Université catholique de Louvain (Belgium). The ground truth data sets required to train supervised classifiers are usually collected as to maximize the number of samples…
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