Zobrazeno 1 - 10
of 884
pro vyhledávání: '"V. Walter"'
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-2024, Pp 83-90 (2024)
In this study, we introduce a crowd-driven data enhancement strategy for the integration of polygons in paid crowdsourcing. First, we capture redundant polygons with a web-based tool using one set of crowdworkers. Then, we present the acquired polygo
Externí odkaz:
https://doaj.org/article/ec91e7838bcd45d3864b39b2061eb640
Autor:
G. Strandberg, P. Blomqvist, N. Fransson, L. Göransson, J. Hansson, S. Hellsten, E. Kjellström, C. Lin, E. Löfblad, S. Montin, E. Nyholm, A. Sandgren, T. Unger, V. Walter, J. Westerberg
Publikováno v:
Climate Services, Vol 34, Iss , Pp 100486- (2024)
Climate change concerns the energy sector to a high degree because the sector is sensitive both to changing conditions for power and heat production, and to changing demand for electricity, heating and cooling. In this study potential consequences of
Externí odkaz:
https://doaj.org/article/0dbccbee180a40b185e4fe5558253e3e
FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 159-166 (2023)
Paid crowdsourcing is a popular approach for creating training data in machine learning, but output quality can suffer from various drawbacks, such as noisy data. One solution is to obtain multiple acquisitions of the same dataset and perform integra
Externí odkaz:
https://doaj.org/article/a9cda8728c08447e94e3a92c735632b7
LEARNING ON THE EDGE: BENCHMARKING ACTIVE LEARNING FOR THE SEMANTIC SEGMENTATION OF ALS POINT CLOUDS
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 945-952 (2023)
While most research in automatic semantic segmentation of 3D geospatial point clouds is concerned with enhancing respective Machine Learning (ML) models, we aim to shift the focus to be more of a data-centric nature. This means, we consider the creat
Externí odkaz:
https://doaj.org/article/332cf20b56254c17842c542a5379c6bc
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2022, Pp 259-266 (2022)
The main bottleneck of machine learning systems, such as convolutional neural networks, is the availability of labeled training data. Hence, much effort (and thus cost) is caused by setting up proper training data sets. However, models trained on spe
Externí odkaz:
https://doaj.org/article/08dd6d2566f54f6d8a9df32afe2ddb5f
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-4-2022, Pp 113-120 (2022)
Non-commercial, unpaid crowdsourcing is the basis of many non-profit projects on the Internet such as Wikipedia or OpenStreetMap. A prerequisite for such projects to be successful is to find a sufficient number of volunteer crowdworkers who are intri
Externí odkaz:
https://doaj.org/article/847288b13ff24f2299a8a1f5061bc99f
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2021, Pp 93-100 (2021)
Semantic interpretation of multi-modal datasets is of great importance in many domains of geospatial data analysis. However, when training models for automated semantic segmentation, labeled training data is required and in case of multi-modality for
Externí odkaz:
https://doaj.org/article/917754f217bb48c4abd2b9da3a52ae6d
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-4-2021, Pp 97-104 (2021)
In this article, we present a two-level approach for the crowd-based collection of vehicles from 3D point clouds. In the first level, the crowdworkers are asked to identify the coarse positions of vehicles in 2D rasterized shadings that were derived
Externí odkaz:
https://doaj.org/article/7601bb468d064acdb76beef64e757fbf
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-4-2020, Pp 49-56 (2020)
The term "Crowdsourcing" goes back to Jeff Howe (Howe, 2006) and represents a neologism of the words "crowd" and "outsourcing". Unlike outsourcing, where companies outsource certain tasks to known third parties, crowdsourcing outsources tasks to unkn
Externí odkaz:
https://doaj.org/article/1c02623bd8604e468c3f0fd1da782d42
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 501-508 (2020)
Automated semantic interpretation of 3D point clouds is crucial for many tasks in the domain of geospatial data analysis. For this purpose, labeled training data is required, which has often to be provided manually by experts. One approach to minimiz
Externí odkaz:
https://doaj.org/article/584112dffa8040c28de5da6404b42d24