Peter Caccetta

66 papers receiving 2.3k citations

Peter Caccetta's Hit Papers

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data 2020 · 1.3k citations
1.3k0+2+4Years since publication4008001.2k

Peers

Peter Caccetta
Comparison fields: 5 of 136
  • Media Technology 517
  • Environmental Engineering 688
  • Computer Vision and Pattern Recognition 675
  • Ecology 600
  • Global and Planetary Change 392
Replace François Waldner with:
François Waldner Belgium
Ying Sun China
Olaf Hellwich Germany
Weitao Chen China
Shenghui Fang China
Wei Han China
Raul Queiroz Feitosa Brazil
Xianju Li China
Silvana Dellepiane Italy
Peter Caccetta relative to François Waldner Belgium François Waldner's profile →
Citations per field
00.5×1.5×
François Waldner · 1×
Citations per year

Countries citing papers authored by Peter Caccetta

Since Specialization
Citations

This map shows the geographic impact of Peter Caccetta's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peter Caccetta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Caccetta more than expected).

Fields of papers citing papers by Peter Caccetta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Caccetta. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peter Caccetta. The network helps show where Peter Caccetta may publish in the future.

Co-authors

The 25 scholars most cited alongside Peter Caccetta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peter Caccetta Line = papers co-authored together Peter Caccetta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Hit paper breakdown →
20201300
2 201493
3 201291
4 202069
5 200258
6 201152
7 202042
8 201041
9 201341
10 201036
11 202236
12 200432
13 202030
14 201430
15 202128
16 201128
17 202126
18 201024
19
The Land Monitor Project
200023
20 200023

About Peter Caccetta

Peter Caccetta is a scholar working on Environmental Engineering, Ecology, Artificial Intelligence, Aerospace Engineering and Global and Planetary Change, having authored 68 papers that have together received 2.3k indexed citations. Recurring topics across this work include Remote Sensing and LiDAR Applications (28 papers), Remote Sensing in Agriculture (26 papers), Soil Geostatistics and Mapping (26 papers), Geochemistry and Geologic Mapping (9 papers), Land Use and Ecosystem Services (8 papers), Synthetic Aperture Radar (SAR) Applications and Techniques (8 papers), Urban Heat Island Mitigation (8 papers) and Remote-Sensing Image Classification (6 papers). The work is most often cited by research in Media Technology (517 citations), Environmental Engineering (688 citations), Computer Vision and Pattern Recognition (675 citations), Ecology (600 citations) and Global and Planetary Change (392 citations). Peter Caccetta has collaborated with scholars based in Australia, China and Greece. Frequent co-authors include Chen Wu, Foivos I. Diakogiannis, François Waldner, Eric Lehmann, J. Wallace, Suzanne Furby, Xiaoliang Wu, Zheng-Shu Zhou, Riccardo Paolini and Hassan Saeed Khan. Their work appears in journals such as International Journal of Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, Journal of Environmental Quality and Remote Sensing of Environment.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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