Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.
Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification
