|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | An empirical study on the maintenance of source code clones 显示文摘 | THUMMALAPENTA S CERULO L AVERSANO L | 2010 | Empirical Software Engineering2010，15，1： | 1 |
| 2 | An empirical study oa the maintenance of source code clones显示文摘 | THUMMALAPENTA S CERULO L AVERSANO L | 2009 | Em- pirical Software Engineering2009，15，1： | 1 |
| 3 | Identity Construction : New Issues, New Directions 显示文摘 | Cerulo Karen A | 1997 | Annual Review of Sociology1997，，23： | 1 |
| 4 | Identity Construction: New Issues, New Directions显示文摘 | K | 1997 | Annual review of sociology1997，，： | 1 |
| 5 | Achievements and challenges in software reverse engineering显示文摘 | Gerardo Canfora Massimiliano Di Penta Luigi Cerulo | 2011 | Communications of the ACM2011，，4： | 1 |
| 6 | An empirical study on the maintenance of source code clones 显示文摘 | THUMMALAPENTA S CERULO L AVERSANO L | 2009 | Em- pirical Software Engineering2009，15，1： | 1 |
| 7 | Tracking your changes: A lan- guage-independent approach 显示文摘 | Canfora G Cerulo L Di P | 2009 | IEEE Software2009，26，1： | 1 |
| 8 | An empirical study on the maintenance of source code clones显示文摘 | THUMMALAPENTA S CERULO L AVERSANO L | 2009 | Em- pirical Software Engineering2009，15，1： | 1 |
| 9 | An empirical study on the maintenance of source code clones 显示文摘 | THUMMALAPENTA S CERULO L AVERSANO L | 2010 | Em- pirical Software Engineering2010，15，1： | 1 |
| 10 | Identity Construction:New Issues, New Directions显示文摘 | CERULO Karen A | 1997 | Annual Reviews Social1997，，23： | 1 |
| 11 | Precision silviculture:use of UAVs and comparison of deep learning models for the identification and segmentation of tree crowns in pine crops显示文摘The monitoring of trees is crucial for the management of large areas of forest cultivations,but this process may be costly.However,remotely sensed data offers a solution to automate this process.In this work,we used two neural network methods named You Only Look Once(YOLO)and Mask R-CNN to overcome the challenging tasks of counting,detecting,and segmenting high dimensional Red–Green–Blue(RGB)images taken from unmanned aerial vehicles(UAVs).We present a processing framework,which is suitable to generate accurate predictions for the aforementioned tasks using a reasonable amount of labeled data.We compared our method using forest stands of different ages and densities.For counting,YOLO overestimates 8.5%of the detected trees on average,whereas Mask R-CNN overestimates a 4.7%of the trees.For the detection task,YOLO obtains a precision of 0.72 and a recall of 0.68 on average,while Mask R-CNN obtains a precision of 0.82 and a recall of 0.80.In segmentation,YOLO overestimates a 13.5%of the predicted area on average,whereas Mask R-CNN overestimates a 9.2%.The proposed methods present a cost-effective solution for forest monitoring using RGB images and have been successfully used to monitor∼146,500 acres of pine cultivations. | Manuel Ignacio Perez Bruno Karelovic Roberto Molina Rodrigo Saavedra Pierluigi Cerulo Guillermo Cabrera | 2022 | International Journal of Digital Earth2022，15，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员