Zobrazeno 1 - 10
of 46 977
pro vyhledávání: '"aerial imagery"'
The robust and safe operation of automated vehicles underscores the critical need for detailed and accurate topological maps. At the heart of this requirement is the construction of lane graphs, which provide essential information on lane connectivit
Externí odkaz:
http://arxiv.org/abs/2407.05687
Autor:
Guerrero, Jesus
This work shows a procedural method for extracting object heights from LiDAR and aerial imagery. We discuss how to get heights and the future of LiDAR and imagery processing. SOTA object segmentation allows us to take get object heights with no deep
Externí odkaz:
http://arxiv.org/abs/2408.00967
Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localizati
Externí odkaz:
http://arxiv.org/abs/2406.00885
Publikováno v:
44th Asian Conference on Remote Sensing, ACRS 2023. Code 198676
In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification
Externí odkaz:
http://arxiv.org/abs/2406.14220
Accurate and efficient label of aerial images is essential for informed decision-making and resource allocation, whether in identifying crop types or delineating land-use patterns. The development of a comprehensive toolbox for manipulating and annot
Externí odkaz:
http://arxiv.org/abs/2406.05833
Aerial imagery is increasingly used in Earth science and natural resource management as a complement to labor-intensive ground-based surveys. Aerial systems can collect overlapping images that provide multiple views of each location from different pe
Externí odkaz:
http://arxiv.org/abs/2405.09544
Autor:
Verma, Tushar, Singh, Jyotsna, Bhartari, Yash, Jarwal, Rishi, Singh, Suraj, Singh, Shubhkarman
Small object detection in aerial imagery presents significant challenges in computer vision due to the minimal data inherent in small-sized objects and their propensity to be obscured by larger objects and background noise. Traditional methods using
Externí odkaz:
http://arxiv.org/abs/2405.01699
Autor:
Gaydon, Charles, Roche, Floryne
Knowledge of tree species distribution is fundamental to managing forests. New deep learning approaches promise significant accuracy gains for forest mapping, and are becoming a critical tool for mapping multiple tree species at scale. To advance the
Externí odkaz:
http://arxiv.org/abs/2404.12064
Autor:
Kalluri, Tarun, Lee, Jihyeon, Sohn, Kihyuk, Singla, Sahil, Chandraker, Manmohan, Xu, Joseph, Liu, Jeremiah
We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. While significant recent advances have resulted in impro
Externí odkaz:
http://arxiv.org/abs/2405.13779
Autor:
Zhang, Shun1 (AUTHOR) liyupeng1006@mail.nwpu.edu.cn, Li, Yupeng1 (AUTHOR), Wu, Xiao1 (AUTHOR), Chu, Zunheng1 (AUTHOR), Li, Lingfei1 (AUTHOR)
Publikováno v:
Remote Sensing. Apr2024, Vol. 16 Issue 7, p1216. 23p.