Ditapis dengan
Instance segmentation of individual tree crowns with YOLOv5: A comparison of …
Fine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss of biodiversity in forest ecosystems. Such information on individual tree crowns (ITC's) can be derived from the application of ITC segmentation approaches, which utilize remotely sensed data. However,…
- Edisi
- Vol.9, August 2023
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 15 hlm PDF, 9.848 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Step towards inter-operable Unmanned Aerial Vehicles (UAV) based phenotyping…
The Unmanned aerial vehicles (UAVs) - based imaging is being intensively explored for precise crop evaluation. Various optical sensors, such as RGB, multi-spectral, and hyper-spectral cameras, can be used for this purpose. Consistent image quality is crucial for accurate plant trait prediction (i.e., phenotyping). However, achieving consistent image quality can pose a challenge as image qualiti…
- Edisi
- Vol.9, August 2023
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 13 hlm PDF, 4.831 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Hybrid georeferencing of images and LiDAR data for UAV-based point cloud coll…
During the last two decades, UAV emerged as standard platform for photogrammetric data collection. Main motivation in that early phase was the cost effective airborne image collection at areas of limited size. This was already feasible by rather simple payloads like an off-the-shelf, compact camera and a navigation-grade GNSS sensor. Meanwhile, dedicated sensor systems enable applications that …
- Edisi
- Vol.4, April 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 11 hlm PDF, 13.880 PDF
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution…
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 3D datas…
- Edisi
- Vol.1, October 2021
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 11 hlm PDF., 8,202 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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