Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications
Problem
Extracting fine-grained features and objects from very high resolution Earth observation data is needed to better understand land-surface dynamics.
Why it matters
Fine-grained EO analysis improves environmental monitoring and understanding of land-surface change.
What the AI did
This is a review of 429 studies using convolutional neural networks for image segmentation and object detection in Earth observation, examining sensors, datasets, architectures and applications.
Results
429 studies reviewed; main finding that CNNs are in an advanced transition phase from computer vision to EO.
Impact
Documents the breadth of CNN adoption across EO applications per the abstract.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer |
| Citations | 177 (OpenAlex, at import time) |
| References | 412 |
| Model or method | convolutional neural networks (CNNs) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs12183053 |
| Open access | gold |
| Keywords | Earth observation, CNN, object detection, image segmentation, review, deep learning |
Importance rating
real but narrower advance
Fine-grained EO analysis improves environmental monitoring and land-surface understanding; abstract quantifies scope as a review of 429 CNN studies.
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