0924-2716
Published by: Elsevier
https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 43 | 186 | 452 | 64 |
| Environmental Sciences | 46 | 203 | 274 | 59 |
Isprs Journal of Photogrammetry and Remote Sensing was organized to reinforce research efforts on Remote sensing, Artificial intelligence, Computer vision, Point cloud and Pattern recognition. Topics in Remote sensing were tackled in line with various other fields like Land cover, Satellite and Vegetation. It emphasizes research on Vegetation, which includes concerns such as Normalized Difference Vegetation Index.
Isprs Journal of Photogrammetry and Remote Sensing primarily discusses Artificial intelligence topics, particularly Deep learning, Pixel, Photogrammetry, Segmentation and Convolutional neural network. It dives deep in exploring the relationship between the study of Pixel and Image resolution. The Computer vision study featured in Isprs Journal of Photogrammetry and Remote Sensing draws connections with the study of Point (geometry).
The research on Point cloud tackled can also make contributions to studies in the areas of Algorithm and Laser scanning. It centers on topics in Pattern recognition, with a focus on Hyperspectral imaging.
The main points discussed in the journal articles deal with Remote sensing, Artificial intelligence, Computer vision, Point cloud and Laser scanning. The journal articles with studies in Remote sensing featured incorporate elements of Land cover and Vegetation. Most of the Artificial intelligence studies addressed in the journal papers also intersect with Pattern recognition.
The main research concerns discussed in Isprs Journal of Photogrammetry and Remote Sensing are Artificial intelligence, Remote sensing, Deep learning, Point cloud and Pattern recognition. The work on Artificial intelligence tackled in it brings together disciplines like Machine learning and Computer vision. The journal tackles studies in Scale (map) and the interrelated subject of Remote sensing (archaeology) to gain insights into Remote sensing.
Deep learning research presented in the journal encompasses a variety of subjects, including Context (language use), Data mining, Satellite imagery, Artificial neural network and Pixel. The research on Point cloud featured in the journal combines topics in other fields like Photogrammetry, Lidar, Point (geometry) and Algorithm. Pattern recognition research presented in Isprs Journal of Photogrammetry and Remote Sensing encompasses a variety of subjects, including Tree (data structure) and Change detection.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Isprs Journal of Photogrammetry and Remote Sensing (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Isprs Journal of Photogrammetry and Remote Sensing (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 3.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 40.25% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.75% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.41% of all publications and 35.59% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
While the research conducted at Isprs Journal of Photogrammetry and Remote Sensing is predominantly pertaining to remote sensing, artificial intelligence, computer vision, point cloud, pattern recognition, among others, there lies a significant interest in diversifying the application of these research insights. It can extend to not only shaping the landscape of traditional science and technology-based sectors but also making a substantial impact in domains such as education. For instance, scanning technology could be used to create detailed 3D models of classroom principals for training purposes. Algorithms developed for pattern recognition could be deployed to analyze the effectiveness of certain teaching methods and provide personalized feedback. Additionally, AI could potentially be used to develop advanced learning apps that adapt to the learning style of each student. A practitioner in the education field who could significantly benefit from these advancements is a preschool teacher assistant. In the context of Hawaii, where nature and technology often intersect, the role of a preschool teacher assistant has the potential to be amplified by these technological advancements. To explore more on becoming a preschool teacher assistant in Hawaii and their evolving role with technological advancements, you can refer to this comprehensive guide how to become a preschool teacher assistant in Hawaii. By highlighting these potential crossover areas, we aim to encourage a multidisciplinary approach in research that could bridge the gap between breakthrough technologies and their real-life applications. This approach could open wider horizons for ambitious professionals navigating their career paths and usher in new possibilities within the research landscape.
Ke Li;Gang Wan;Gong Cheng;Liqiu Meng
(2020)Teja Kattenborn;Jens Leitloff;Felix Schiefer;Stefan Hinz
(2021)Haifa Tamiminia;Bahram Salehi;Masoud Mahdianpari;Lindi Quackenbush
(2020)Collin G. Homer;Jon Dewitz;Suming Jin;George Z. Xian
(2020)Unknown
(2021)Zhen Dong;Fuxun Liang;Bisheng Yang;Yusheng Xu
(2020)Dehua Mao;Zongming Wang;Baojia Du;Lin Li
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