Zobrazeno 1 - 10
of 701
pro vyhledávání: '"P. Krzystek"'
Publikováno v:
ISPRS Open Journal of Photogrammetry and Remote Sensing, Vol 8, Iss , Pp 100037- (2023)
Precise single tree delineation allows for a more reliable determination of essential parameters such as tree species, height and vitality. Methods of instance segmentation are powerful neural networks for detecting and segmenting single objects and
Externí odkaz:
https://doaj.org/article/3c721d92ca8f4b3e97fcc5a811afc5b4
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2022, Pp 981-988 (2022)
Single tree detection has been a major research topic concerning automatic forest inventory using remote sensing data. Recently, deep learning-based approaches in remote sensing forestry have gained attention because of the prospect of improved accur
Externí odkaz:
https://doaj.org/article/ca0da0c4f267462eb9e531bf1717bbda
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 203-210 (2020)
Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of
Externí odkaz:
https://doaj.org/article/f78f07959b4a4155959b8d34f6f9ff0a
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 98, Iss , Pp 102292- (2021)
Forest managers and nature conservationists rely on precise mapping of single trees from remote sensing data for efficient estimation of forest attributes. In recent years, additional quantification of dead wood in particular has garnered interest. H
Externí odkaz:
https://doaj.org/article/d2d0b2b1bbf74ef98b27ee833bbd7fa1
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W13, Pp 951-955 (2019)
Most methods for the mapping of tree species are based on the segmentation of single trees that are subsequently classified using a set of hand-crafted features and an appropriate classifier. The classification accuracy for coniferous and deciduous t
Externí odkaz:
https://doaj.org/article/0540a09d3574403fb744d5f127d983d1
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2, Pp 163-169 (2018)
Shortly after the explosion of the Chernobyl nuclear power plant (ChNPP) in 1986, radioactive fall-out and contaminated trees (socalled Red Forest) were buried in the Chernobyl Exclusion Zone (ChEZ). These days, exact locations of the buried contamin
Externí odkaz:
https://doaj.org/article/1aaac04dda3c4728898cbe93fb70f64d
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-3, Pp 31-34 (2018)
The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three differe
Externí odkaz:
https://doaj.org/article/f01c93fe42f54418bd5a1993ef91a1b5
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W7, Pp 971-976 (2017)
In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of
Externí odkaz:
https://doaj.org/article/e3dbb38b14924c52b206e7356c75b352
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2-W4, Pp 35-42 (2017)
Airborne Laser Scanning (ALS) is a widespread method for forest mapping and management purposes. While common ALS techniques provide valuable information about the forest canopy and intermediate layers, the point density near the ground may be poor
Externí odkaz:
https://doaj.org/article/8588aba543cb4537b5281edbf5cbbc58
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLI-B7, Pp 405-410 (2016)
The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban
Externí odkaz:
https://doaj.org/article/3640cb0a03c34ac8a94b9bfd5089866d