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Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks

https://doi.org/10.1109/lgrs.2015.2499239

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Outgoing claims: the papers this work cites, builds on, or argues with.

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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

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Deep Residual SVM: A Hybrid Learning Approach to obtain High Discriminative Feature for Land Use and Land Cover Classification

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Decoding urban complexity: Deep learning-based terrain-specific building segmentation for Indian cities

inferred← citeshttps://doi.org/10.1016/j.rse.2018.04.050openalex-snapshot

Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

inferred← citeshttps://doi.org/10.1016/j.rse.2019.111322openalex-snapshot

Land-cover classification with high-resolution remote sensing images using transferable deep models

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A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution

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Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope

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Transfer learning in environmental remote sensing

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Deep learning for urban land use category classification: A review and experimental assessment

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Enhancing motor imagery decoding via transfer learning

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Learning with less: label-efficient land cover classification at very high spatial resolution using self-supervised deep learning

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Convolution neural network-based estimation of high-resolution urban canopy parameters for urban climate modelling

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Large-scale Landsat image classification based on deep learning methods

inferred← citeshttps://doi.org/10.1029/2018wr022643openalex-snapshot

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

inferred← citeshttps://doi.org/10.1029/2020wr028600openalex-snapshot

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

inferred← citeshttps://doi.org/10.1029/2025ef006060openalex-snapshot

Enhancing Hydrological Extremes Forecasting Capabilities in Data‐Scarce Regions Through Transfer Learning With Data Augmentation

inferred← citeshttps://doi.org/10.1029/2025wr041818openalex-snapshot

Applying Transfer Learning for Street‐Scale Nuisance Flood Forecasting in Coastal‐Urban Environments

inferred← citeshttps://doi.org/10.1038/s41598-020-78129-0openalex-snapshot

Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

inferred← citeshttps://doi.org/10.1038/s41598-026-46887-yopenalex-snapshot

A few-shot high-resolution remote sensing image semantic segmentation method

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Recurrent neural networks for remote sensing image classification

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Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing

inferred← citeshttps://doi.org/10.1051/0004-6361/201834575openalex-snapshot

Morphology-assisted galaxy mass-to-light predictions using deep learning

inferred← citeshttps://doi.org/10.1051/0004-6361/202141360openalex-snapshot

Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

inferred← citeshttps://doi.org/10.1063/5.0155152openalex-snapshot

Hyperspectral image ground-object identification method based on spectral segment fusion combination and depth residual network

inferred← citeshttps://doi.org/10.1073/pnas.2304032120openalex-snapshot

The global biogeography and environmental drivers of fairy circles

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Using convolutional features and a sparse autoencoder for land-use scene classification

inferred← citeshttps://doi.org/10.1080/01431161.2016.1266059openalex-snapshot

Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery

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A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification

inferred← citeshttps://doi.org/10.1080/01431161.2017.1399472openalex-snapshot

Visual descriptors for content-based retrieval of remote-sensing images

inferred← citeshttps://doi.org/10.1080/01431161.2018.1516313openalex-snapshot

Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series

inferred← citeshttps://doi.org/10.1080/01431161.2018.1563840openalex-snapshot

Automated LULC map production using deep neural networks

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Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images

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An auto-encoder based classifier for crop mapping from multitemporal multispectral imagery

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SAR image edge detection: review and benchmark experiments

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Coupling of deep learning and remote sensing: a comprehensive systematic literature review

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A literature review on remote sensing scene categorization based on convolutional neural networks

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Multispectral Satellite-Derived Bathymetry Based on Sparse Prior Measured Data

inferred← citeshttps://doi.org/10.1080/08839514.2021.2014192openalex-snapshot

Transfer Learning Models for Land Cover and Land Use Classification in Remote Sensing Image

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Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold

inferred← citeshttps://doi.org/10.1080/10106049.2023.2252389openalex-snapshot

Deep learning methods applied to digital elevation models: state of the art

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Hybrid deep learning and boosting for multi-source coastal scene classification using aerial imagery

inferred← citeshttps://doi.org/10.1080/10584587.2022.2065568openalex-snapshot

Defect Detection and Classification Algorithm of Metal Nanomaterials Based on Deep Learning

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Enhancing building extraction from remote sensing images through UNet and transfer learning

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Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks

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Review of CNN in aerial image processing

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Deep Learning for the Analysis of Disruption Precursors Based on Plasma Tomography

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A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment

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Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images

inferred← citeshttps://doi.org/10.1080/19498276.2023.2251982openalex-snapshot

Picture This: A Deep Learning Model for Operational Real Estate Emissions

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A survey of remote sensing image classification based on CNNs

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Scene classification in remote sensing images using a two-stage neural network ensemble model

inferred← citeshttps://doi.org/10.1080/2150704x.2026.2682395openalex-snapshot

Multi-scale attention network for water body detection using sparse point labels

inferred← citeshttps://doi.org/10.1080/21681163.2025.2539079openalex-snapshot

Enhancing diabetic retinopathy classification accuracy through dual-attention mechanism in deep learning

inferred← citeshttps://doi.org/10.1080/22797254.2019.1581582openalex-snapshot

Designing deep CNN models based on sparse coding for aerial imagery: a deep-features reduction approach

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An enhanced back propagation method for change analysis of remote sensing images with adaptive preprocessing

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Machine learning-based multimodal data fusion for the Swiss land use statistics

inferred← citeshttps://doi.org/10.1080/29979676.2024.2401756openalex-snapshot

Mapping Conservation Practices via Deep Learning: Improving Performance via Hillshade Imagery, Sampling Design, and Centerline Dice Loss

inferred← citeshttps://doi.org/10.1088/1402-4896/ae6490openalex-snapshot

Intelligent identification of nonlinear parameters in negative stiffness mechanism using SCINet

showing the first 200 of 639

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