FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366991553> ?p ?o ?g. }

• W4366991553 endingPage "2263" @default.
• W4366991553 startingPage "2263" @default.
• W4366991553 abstract "When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques." @default.
• W4366991553 created "2023-04-27" @default.
• W4366991553 creator A5039379115 @default.
• W4366991553 creator A5075650930 @default.
• W4366991553 creator A5085343517 @default.
• W4366991553 date "2023-04-25" @default.
• W4366991553 modified "2023-10-14" @default.
• W4366991553 title "Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications" @default.
• W4366991553 cites W1981527205 @default.
• W4366991553 cites W1987267031 @default.
• W4366991553 cites W1998943389 @default.
• W4366991553 cites W2019118234 @default.
• W4366991553 cites W2041783407 @default.
• W4366991553 cites W2066416082 @default.
• W4366991553 cites W2138126656 @default.
• W4366991553 cites W2150220236 @default.
• W4366991553 cites W2159105546 @default.
• W4366991553 cites W2165148193 @default.
• W4366991553 cites W2296685749 @default.
• W4366991553 cites W2521144071 @default.
• W4366991553 cites W2556502614 @default.
• W4366991553 cites W2574426255 @default.
• W4366991553 cites W2591466624 @default.
• W4366991553 cites W2594502636 @default.
• W4366991553 cites W2594847594 @default.
• W4366991553 cites W2603798200 @default.
• W4366991553 cites W2606100861 @default.
• W4366991553 cites W2608264083 @default.
• W4366991553 cites W2617056706 @default.
• W4366991553 cites W2726000536 @default.
• W4366991553 cites W2726735347 @default.
• W4366991553 cites W2736508163 @default.
• W4366991553 cites W2740121437 @default.
• W4366991553 cites W2743601682 @default.
• W4366991553 cites W2747779538 @default.
• W4366991553 cites W2752278344 @default.
• W4366991553 cites W2765366036 @default.
• W4366991553 cites W2765920222 @default.
• W4366991553 cites W2766361846 @default.
• W4366991553 cites W2767198899 @default.
• W4366991553 cites W2768000522 @default.
• W4366991553 cites W2770340534 @default.
• W4366991553 cites W2773589087 @default.
• W4366991553 cites W2780578005 @default.
• W4366991553 cites W2789860026 @default.
• W4366991553 cites W2792846923 @default.
• W4366991553 cites W2801130496 @default.
• W4366991553 cites W2804625175 @default.
• W4366991553 cites W2886384139 @default.
• W4366991553 cites W2887452256 @default.
• W4366991553 cites W2891371927 @default.
• W4366991553 cites W2908563588 @default.
• W4366991553 cites W2941004385 @default.
• W4366991553 cites W2948027403 @default.
• W4366991553 cites W2971586224 @default.
• W4366991553 cites W2971872054 @default.
• W4366991553 cites W2979970650 @default.
• W4366991553 cites W2990265654 @default.
• W4366991553 cites W2995081987 @default.
• W4366991553 cites W2996445195 @default.
• W4366991553 cites W2997224422 @default.
• W4366991553 cites W2998296266 @default.
• W4366991553 cites W2998595112 @default.
• W4366991553 cites W2998891120 @default.
• W4366991553 cites W2999114949 @default.
• W4366991553 cites W3000696750 @default.
• W4366991553 cites W3001558022 @default.
• W4366991553 cites W3004769717 @default.
• W4366991553 cites W3009434547 @default.
• W4366991553 cites W3010715462 @default.
• W4366991553 cites W3010920006 @default.
• W4366991553 cites W3011596392 @default.
• W4366991553 cites W3012039216 @default.
• W4366991553 cites W3013079618 @default.
• W4366991553 cites W3014283326 @default.
• W4366991553 cites W3014525787 @default.
• W4366991553 cites W3015952109 @default.
• W4366991553 cites W3016663000 @default.
• W4366991553 cites W3017035390 @default.
• W4366991553 cites W3017365251 @default.
• W4366991553 cites W3022155827 @default.
• W4366991553 cites W3028875059 @default.
• W4366991553 cites W3032296161 @default.
• W4366991553 cites W3038016698 @default.
• W4366991553 cites W3043859127 @default.
• W4366991553 cites W3044895230 @default.
• W4366991553 cites W3045909161 @default.
• W4366991553 cites W3046083520 @default.
• W4366991553 cites W3046837470 @default.
• W4366991553 cites W3047099884 @default.
• W4366991553 cites W3081976154 @default.
• W4366991553 cites W3083250692 @default.
• W4366991553 cites W3087613434 @default.
• W4366991553 cites W3091817285 @default.
• W4366991553 cites W3092153688 @default.
• W4366991553 cites W3093252501 @default.
• W4366991553 cites W3094146903 @default.
• W4366991553 cites W3096336428 @default.

• next