MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]

Burak Ekim, Timo T. Stomberg, Ribana Roscher, Michael Schmitt

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The advancement in deep learning (DL) techniques has led to a notable increase in the number and size of annotated datasets in a variety of domains, with remote sensing (RS) being no exception [1]. Also, an increase in Earth observation (EO) missions and the easy access to globally available and free geodata have opened up new research opportunities. Although numerous RS datasets have been published in the past years [2], [3], [4], [5], [6], most of them addressed tasks concerning man-made environments, such as building footprint extraction and road network classification, leaving the environmental and ecology-related subareas of RS underrepresented. Nevertheless, environmental protection has always been an important topic in the RS community, with RS being a useful tool to support conservation policies and strategies combating challenges such as deforestation and loss of biodiversity [7], [8], [9]. Thus, in this article, to meet the pressing need to better understand the nature we are living in, we introduce a novel task of wilderness mapping and advertise the MapInWild dataset [10] - a multimodal large-scale benchmark dataset designed for the task of wilderness mapping from space.

Original languageEnglish
Pages (from-to)103-114
Number of pages12
JournalIEEE Geoscience and Remote Sensing Magazine
Issue number1
StatePublished - 1 Mar 2023
Externally publishedYes


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