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A qualitative study of Machine Learning practices and engineering challenges in Earth Observation

  • Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the responsible development of ML-based EO applications. To improve this situation a better understanding of the current practices and engineering-related challenges in developing ML-based EO applications is required. In this paper, we report observations from an exploratory study where five experts shared their view on ML engineering in semi-structured interviews. We analysed these interviews with coding techniques as often applied in the domain of empirical software engineering. The interviews provide informative insights into the practical development of ML applications and reveal several engineering challenges. In addition, interviewees participated in a novel workflow sketching task, which provided a tangible reflection of implicit processes. Overall, the results confirm a gap between theoretical conceptions and real practices in ML development even though workflows were sketched abstractly as textbook-like. The results pave the way for a large-scale investigation on requirements for ML engineering in EO.

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Document Type:Article
Author:Sophie Jentzsch, Nico Hochgeschwender
Parent Title (English):it (Information Technology)
First Page:235
Last Page:247
Publisher:De Gruyter Oldenbourg
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2021/07/15
Funding:This work was supported by the DLR “KI Softwaretechnik für die Erdbeobachtung” (AI software technology for earth observation) project.
Keyword:Artificial Intelligence; Earth Observation; Machine Learning; Process Models
Departments, institutes and facilities:Fachbereich Informatik
Institut für Cyber Security & Privacy (ICSP)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2021/07/27
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International