21,029 research outputs found
Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya
The utilisation of big data has emerged as a critical instrument for land use classification and decision-making processes due to its high spatiotemporal accuracy and ability to diminish manual data collection. However, the reliability and feasibility of big data are still controversial, the most important of which is whether it can represent the whole population with justice. The present study incorporates multiple data sources to facilitate land use classification while proving the existence of data bias caused digital injustice. Using Nairobi, Kenya, as a case study and employing a random forest classifier as a benchmark, this research combines satellite imagery, night-time light images, building footprint, Twitter posts, and street view images. The findings of the land use classification also disclose the presence of data bias resulting from the inadequate coverage of social media and street view data, potentially contributing to injustice in big data-informed decision-making. Strategies to mitigate such digital injustice situations are briefly discussed here, and more in-depth exploration remains for future work
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Urban detection, delimitation and morphology: comparative analysis of selective "megacities"
Postprint (published version
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