Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Markku Luotamo"'
Autor:
Maria Yli-Heikkilä, Samantha Wittke, Markku Luotamo, Eetu Puttonen, Mika Sulkava, Petri Pellikka, Janne Heiskanen, Arto Klami
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4193 (2022)
One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method
Externí odkaz:
https://doaj.org/article/ed7a4e1703df457eaea3cadd9672e99b
Publikováno v:
Applied Sciences, Vol 12, Iss 2, p 679 (2022)
We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and
Externí odkaz:
https://doaj.org/article/04fc790d3bf040f388bfc35e7ad7c119
Autor:
Petri Pellikka, Markku Luotamo, Niklas Sädekoski, Jesse Hietanen, Ilja Vuorinne, Matti Räsänen, Janne Heiskanen, Mika Siljander, Kristiina Karhu, Arto Klami
The largest actively cycling terrestrial carbon pool, soil, has been disturbed during the last centuries by human actions through decreasing woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61a665a08561619a3ff22ee1fd26a09c
http://hdl.handle.net/10138/357718
http://hdl.handle.net/10138/357718
Autor:
Klami, Maria Yli-Heikkilä, Samantha Wittke, Markku Luotamo, Eetu Puttonen, Mika Sulkava, Petri Pellikka, Janne Heiskanen, Arto
Publikováno v:
Remote Sensing; Volume 14; Issue 17; Pages: 4193
One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::2d35061679ba897a98f279e8188727f1
Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bd1cefc5d98b3afcdcc6e1f02aa47db