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ISPRS Open Journal of Photogrammetry and Remote Sensing is a peer-reviewed journal published by Elsevier, covering Remote Sensing and LiDAR Applications, 3D Surveying and Cultural Heritage, Remote Sensing in Agriculture.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, ISPRS Open Journal of Photogrammetry and Remote Sensing 12 (2023) 45–58.
Formats any DOI in ISPRS Open Journal of Photogrammetry and Remote Sensing style. No sign-up. |
| Publishes research in | Remote Sensing and LiDAR Applications 3D Surveying and Cultural Heritage Remote Sensing in Agriculture Robotics and Sensor-Based Localization Remote-Sensing Image Classification |
| ISSN | 2667-3932 |
| Citation impact (2-yr) | 4.48 |
| h-index | 21 |
| i10-index | 36 |
| Total citations | 1,526 |
| Article processing charge | $1,800 |
| Open access | Yes |
| Top institutions publishing here | Finnish Geospatial Research Institute |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
In recent years, there has been a growing emphasis on assessing and ensuring the quality of horticultural and agricultural produce. Traditional methods involving field measurements, investigations, and statistical analyses are labour-intensive, time-consuming, and costly. As a solution, Hyperspectral Imaging (HSI) has emerged as a non-destructive and environmentally friendly technology. HSI has gained significant popularity as…
Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cross-validation, which does not account for spatial…
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated…
Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid…
Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting tree mortality and has been proven effective for the assessment of abrupt and large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing tree mortality events (e.g., due to drought) are…
