Daniel Moraes

516 total citations
25 papers, 336 citations indexed

About

Daniel Moraes is a scholar working on Ecology, Media Technology and Computer Networks and Communications. According to data from OpenAlex, Daniel Moraes has authored 25 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Ecology, 8 papers in Media Technology and 6 papers in Computer Networks and Communications. Recurrent topics in Daniel Moraes's work include Remote Sensing in Agriculture (11 papers), Remote-Sensing Image Classification (7 papers) and Land Use and Ecosystem Services (5 papers). Daniel Moraes is often cited by papers focused on Remote Sensing in Agriculture (11 papers), Remote-Sensing Image Classification (7 papers) and Land Use and Ecosystem Services (5 papers). Daniel Moraes collaborates with scholars based in Brazil, Portugal and Sweden. Daniel Moraes's co-authors include Anderson Rocha, Sandra Avila, Daniel Moreira, Mauricio Pérez, Siome Goldenstein, Vanessa Testoni, Eduardo Valle, Mário Caetano, Eleri Cardozo and Hugo Costa and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing and Neurocomputing.

In The Last Decade

Daniel Moraes

24 papers receiving 328 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Moraes Brazil	10	155	78	70	48	47	25	336
Silvio Jamil F. Guimarães Brazil	13	342 2.2×	85 1.1×	63 0.9×	17 0.4×	74 1.6×	75	443
Jakaria Rabbi Bangladesh	5	207 1.3×	83 1.1×	91 1.3×	11 0.2×	31 0.7×	10	364
Linghui Li China	9	63 0.4×	70 0.9×	25 0.4×	13 0.3×	28 0.6×	46	244
Liyuan Xing China	9	295 1.9×	38 0.5×	84 1.2×	15 0.3×	90 1.9×	47	405
Ratnadeep R. Deshmukh India	12	94 0.6×	205 2.6×	24 0.3×	29 0.6×	187 4.0×	89	488
Mengmeng Wang China	9	92 0.6×	44 0.6×	108 1.5×	30 0.6×	9 0.2×	39	299
Piotr Biliński Poland	7	157 1.0×	81 1.0×	51 0.7×	17 0.4×	20 0.4×	20	278
Yen‐Hung Chen Taiwan	7	95 0.6×	36 0.5×	53 0.8×	5 0.1×	14 0.3×	33	304
Ankur Jain United States	9	80 0.5×	58 0.7×	14 0.2×	47 1.0×	39 0.8×	22	274

Countries citing papers authored by Daniel Moraes

Since Specialization

Citations

This map shows the geographic impact of Daniel Moraes'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 Daniel Moraes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Moraes more than expected).

Fields of papers citing papers by Daniel Moraes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Moraes. 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 Daniel Moraes. The network helps show where Daniel Moraes may publish in the future.

Co-authorship network of co-authors of Daniel Moraes

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Moraes. A scholar is included among the top collaborators of Daniel Moraes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Moraes. Daniel Moraes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Moraes, Daniel, Manuel L. Campagnolo, & Mário Caetano. (2025). A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data. Remote Sensing. 17(4). 711–711. 2 indexed citations

Moraes, Daniel, et al.. (2024). Continuous forest loss monitoring in a dynamic landscape of Central Portugal with Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation. 130. 103913–103913. 6 indexed citations

Paiva, Rodrigo Cauduro Dias de, Daniel Moraes, Bharat Jayaraman, et al.. (2023). Testing the efficacy of different combinations of time and temperature to inactivate PRRSV and PEDV. 1 indexed citations

Moraes, Daniel, et al.. (2022). Exploring Different Levels of Class Nomenclature in Random Forest Classification of Sentinel-2 Data. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 9. 2279–2282. 1 indexed citations

Costa, Hugo, et al.. (2022). Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge. Remote Sensing. 14(8). 1865–1865. 30 indexed citations

Benevides, Pedro, et al.. (2021). Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 5–5. 2 indexed citations

Moraes, Daniel, et al.. (2021). Influence of Sample Size in Land Cover Classification Accuracy Using Random Forest and Sentinel-2 Data in Portugal. 4232–4235. 11 indexed citations

Moraes, Daniel, et al.. (2021). Assessment of the introduction of spatial stratification and manual training in automatic supervised image classification. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 12. 36–36. 3 indexed citations

Costa, Hugo, et al.. (2021). Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 2. 5807–5810. 3 indexed citations

10.

Benevides, Pedro, et al.. (2021). Annual Crop Classification Experiments in Portugal Using Sentinel-2. 5838–5841. 3 indexed citations

11.

Moreira, Daniel, Sandra Avila, Mauricio Pérez, et al.. (2018). Multimodal data fusion for sensitive scene localization. Information Fusion. 45. 307–323. 17 indexed citations

12.

Moreira, Daniel, Sandra Avila, Mauricio Pérez, et al.. (2016). Pornography classification: The hidden clues in video space–time. Forensic Science International. 268. 46–61. 50 indexed citations

13.

Pérez, Mauricio, Sandra Avila, Daniel Moreira, et al.. (2016). Video pornography detection through deep learning techniques and motion information. Neurocomputing. 230. 279–293. 93 indexed citations

14.

Moraes, Daniel, Jacques Wainer, & Anderson Rocha. (2016). Low false positive learning with support vector machines. Journal of Visual Communication and Image Representation. 38. 340–350. 10 indexed citations

15.

Moreira, Daniel, Sandra Avila, Mauricio Pérez, et al.. (2015). RECOD at MediaEval 2015: Affective Impact of Movies Task. MediaEval. 3 indexed citations

16.

Avila, Sandra, Daniel Moreira, Mauricio Pérez, et al.. (2014). RECOD at MediaEval 2014: Violent Scenes Detection Task.. MediaEval. 9 indexed citations

17.

Cardozo, Eleri, et al.. (2010). Design and Implementation Issues for Modern Remote Laboratories. IEEE Transactions on Learning Technologies. 4(2). 149–161. 51 indexed citations

18.

Moraes, Daniel, et al.. (2009). A Network Architecture for Large Mobile Robotics Environments. 5 indexed citations

19.

Cardozo, Eleri, et al.. (2009). A Network Architecture for Providing Micro-Mobility in MPLS/GMPLS Networks. e88 b. 1–6. 1 indexed citations

20.

Cardozo, Eleri, et al.. (2008). MPA: A Network-Centric Architecture for Micro-Mobility Support in IP and MPLS Networks. 609–616. 6 indexed citations

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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