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Author: Munch, Zahn

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Improving the generalisabiility of a deep learning model for global forest classification through image normalisation, enhancement and augmentation

Similar Items: Improving the generalisabiility of a deep learning model for global forest classification through image normalisation, enhancement and augmentation

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An earth observation approach for mapping indigenous and plantation forests at regional scale
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Discriminating between forest plantation genera using remote sensing and machine learning algorithms
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Proposal and prototype electronics for Sunsat Mark II imager
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A remote sensing-machine learning framework for modelling forest health
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Processing of onboard images to assist automatic forward motion compensation for micro-satellites
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Machine learning and high spatial resolution multi-temporal Sentinel-2 imagery for crop type classification
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2-D and 3-D proximal remote sensing for yield estimation in a Shiraz vineyard
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Concise analysis and testing of a software model of a satellite remote sensing system used for image generation
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Mapping surface water in complex and heterogeneous environments using remote sensing
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Sequential land cover classification
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Using LiDAR derivatives to estimate sediment grain size on beaches in False Bay
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Building identification in aerial imagery using deep learning
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Remote sensing approach for examining changes in water use in relation to climate variability and land cover change
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Geospatial Assessment of Akure Forest Reserve in Ondo State, Nigeria
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Earth observation methods for sustainable Karoo Rangeland Management
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The feasibility of using remote sensing and field-based checks to monitor the impact caused by collection of wood in the Eastern Cape/Ciskei forest and thicket formations
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Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
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Satellite monitoring of post-mining rehabilitation and the quantification of backfill dynamics using differential SAR interferometry
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Onboard image geo-referencing for LEO satellites
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The impact of training set size and feature dimensionality on supervised object-based classification : a comparison of three classifiers
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Assessing the extent of and changes in the wildlife sector in Limpopo province, South Africa
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The use of high-resolution satellite imagery in forest inventory : a case of Hans Kanyinga Community Forest - Namibia
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Deep Learning-Based Change Detection in High-Resolution Remote Sensing Imagery
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The use of UAV derived photogrammetry data as a tool for tree measurement in industrial plantation forestry with the focus on different Local maxima and Machine learning detection methods.