2194-9042
Published by: International Society for Photogrammetry and Remote Sensing
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 411 | 115 | 275 | 14 |
| Environmental Sciences | 481 | 46 | 44 | 10 |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences is mainly concerned with subjects like Artificial intelligence, Computer vision, Remote sensing, Point cloud and Photogrammetry. The studies on Artificial intelligence discussed can also contribute to research in the domains of Machine learning and Pattern recognition. The research on Computer vision featured in the journal combines topics in other fields like Matching (statistics), Process (computing) and Computer graphics (images).
Terrain, Satellite and Vegetation are some topics wherein Remote sensing research discussed in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences have an impact. The journal explores topics in Point cloud which can be helpful for research in disciplines like Lidar, Point (geometry), Data mining and Laser scanning. The work tackled in it goes beyond the discipline of Deep learning as it also encompasses Convolutional neural network.
The journal papers aim to foster the development of research in Artificial intelligence, Computer vision, Point cloud, Photogrammetry and Remote sensing. The most cited publications focus on Artificial intelligence but the discussions also offer insight into other areas such as Machine learning and Pattern recognition. The featured Computer vision studies in the journal articles mainly concentrate on Matching (statistics) but also cover areas of interest in Orthophoto.
The journal focuses on Artificial intelligence, Remote sensing, Point cloud, Computer vision and Segmentation. Some problems in Artificial intelligence that were presented in the journal overlapped with concepts under Machine learning and Pattern recognition. Topics in Remote sensing were tackled in line with various other fields like Pixel and Atmospheric correction.
While Point cloud is the focus of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, it also provided insights into the studies of 3D city models, Data mining, Point (geometry), Documentation and Laser scanning. It primarily discusses Computer vision topics, particularly Photogrammetry, Image (mathematics) and Sensor fusion. While work presented in the journal provided substantial information on Segmentation, it also covered topics in Cluster analysis and Benchmark (computing).
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 Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (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 Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (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, 27.43% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 28.35% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.81% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.17% of all publications and 45.67% 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.
One important segment missing in the article is the application of the discussions in the said journal to real-world situations, such as the field of special education. Emphasizing the application of these scientific studies would strengthen the content's depth and relevance by connecting it to specific use-cases. Here's a draft for this section: **
Artificial intelligence, for instance, can be employed to create adaptive learning systems tailored to the individual needs of special education students. Similarly, Remote sensing and Photogrammetry can play crucial roles in creating interactive and engaging content for these students, enhancing their understanding and learning outcomes.
However, the application of these technologies would require skilled professionals capable of integrating these technologies into education systems. As such, it is pivotal for educators or aspiring professionals to acquire knowledge in these areas. For those interested in becoming a special education teacher in Pennsylvania, obtaining special education certification online Pennsylvania{anchor} could be a vital step towards making a significant difference in this field.
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