Skip to content

Commit c78d61d

Browse files
editorialboteditorialbot
authored
Merge pull request #7311 from openjournals/joss.08056
Merging automatically
2 parents ddfe0a4 + 5903989 commit c78d61d

4 files changed

Lines changed: 881 additions & 0 deletions

File tree

‎joss.08056/10.21105.joss.08056.crossref.xml‎

Lines changed: 254 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,254 @@
1+
<?xml version="1.0" encoding="UTF-8"?>
2+
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
3+
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
4+
xmlns:rel="http://www.crossref.org/relations.xsd"
5+
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
6+
version="5.3.1"
7+
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
8+
<head>
9+
<doi_batch_id>20251020185449-cee87713f4141be96b58e2f4958407bf83ac3524</doi_batch_id>
10+
<timestamp>20251020185449</timestamp>
11+
<depositor>
12+
<depositor_name>JOSS Admin</depositor_name>
13+
<email_address>[email protected]</email_address>
14+
</depositor>
15+
<registrant>The Open Journal</registrant>
16+
</head>
17+
<body>
18+
<journal>
19+
<journal_metadata>
20+
<full_title>Journal of Open Source Software</full_title>
21+
<abbrev_title>JOSS</abbrev_title>
22+
<issn media_type="electronic">2475-9066</issn>
23+
<doi_data>
24+
<doi>10.21105/joss</doi>
25+
<resource>https://joss.theoj.org</resource>
26+
</doi_data>
27+
</journal_metadata>
28+
<journal_issue>
29+
<publication_date media_type="online">
30+
<month>10</month>
31+
<year>2025</year>
32+
</publication_date>
33+
<journal_volume>
34+
<volume>10</volume>
35+
</journal_volume>
36+
<issue>114</issue>
37+
</journal_issue>
38+
<journal_article publication_type="full_text">
39+
<titles>
40+
<title>DeepTrees: Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch</title>
41+
</titles>
42+
<contributors>
43+
<person_name sequence="first" contributor_role="author">
44+
<given_name>Taimur</given_name>
45+
<surname>Khan</surname>
46+
<affiliations>
47+
<institution><institution_name>Helmholtz Center for Environmental Research – UFZ</institution_name></institution>
48+
</affiliations>
49+
<ORCID>https://orcid.org/0000-0001-7833-5474</ORCID>
50+
</person_name>
51+
<person_name sequence="additional"
52+
contributor_role="author">
53+
<given_name>Caroline</given_name>
54+
<surname>Arnold</surname>
55+
<affiliations>
56+
<institution><institution_name>Helmholtz-Zentrum hereon</institution_name></institution>
57+
<institution><institution_name>Helmholtz AI</institution_name></institution>
58+
</affiliations>
59+
<ORCID>https://orcid.org/0000-0002-9458-1517</ORCID>
60+
</person_name>
61+
<person_name sequence="additional"
62+
contributor_role="author">
63+
<given_name>Harsh</given_name>
64+
<surname>Grover</surname>
65+
<affiliations>
66+
<institution><institution_name>Helmholtz-Zentrum hereon</institution_name></institution>
67+
<institution><institution_name>Helmholtz AI</institution_name></institution>
68+
</affiliations>
69+
<ORCID>https://orcid.org/0009-0000-2055-7525</ORCID>
70+
</person_name>
71+
</contributors>
72+
<publication_date>
73+
<month>10</month>
74+
<day>20</day>
75+
<year>2025</year>
76+
</publication_date>
77+
<pages>
78+
<first_page>8056</first_page>
79+
</pages>
80+
<publisher_item>
81+
<identifier id_type="doi">10.21105/joss.08056</identifier>
82+
</publisher_item>
83+
<ai:program name="AccessIndicators">
84+
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
85+
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
86+
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
87+
</ai:program>
88+
<rel:program>
89+
<rel:related_item>
90+
<rel:description>Software archive</rel:description>
91+
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.17371394</rel:inter_work_relation>
92+
</rel:related_item>
93+
<rel:related_item>
94+
<rel:description>GitHub review issue</rel:description>
95+
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/8056</rel:inter_work_relation>
96+
</rel:related_item>
97+
</rel:program>
98+
<doi_data>
99+
<doi>10.21105/joss.08056</doi>
100+
<resource>https://joss.theoj.org/papers/10.21105/joss.08056</resource>
101+
<collection property="text-mining">
102+
<item>
103+
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.08056.pdf</resource>
104+
</item>
105+
</collection>
106+
</doi_data>
107+
<citation_list>
108+
<citation key="freudenberg2022">
109+
<article_title>Individual tree crown delineation in high-resolution remote sensing images based on u-net</article_title>
110+
<author>Freudenberg</author>
111+
<journal_title>Neural Computing and Applications</journal_title>
112+
<issue>24</issue>
113+
<volume>34</volume>
114+
<doi>10.1007/s00521-022-07640-4</doi>
115+
<cYear>2022</cYear>
116+
<unstructured_citation>Freudenberg, M., Magdon, P., &amp; Nölke, N. (2022). Individual tree crown delineation in high-resolution remote sensing images based on u-net. Neural Computing and Applications, 34(24), 22197–22207. https://doi.org/10.1007/s00521-022-07640-4</unstructured_citation>
117+
</citation>
118+
<citation key="zhao2023">
119+
<article_title>A systematic review of individual tree crown detection and delineation with convolutional neural networks (CNN)</article_title>
120+
<author>Zhao</author>
121+
<journal_title>Current Forestry Reports</journal_title>
122+
<issue>3</issue>
123+
<volume>9</volume>
124+
<doi>10.1007/s40725-023-00184-3</doi>
125+
<cYear>2023</cYear>
126+
<unstructured_citation>Zhao, H., Morgenroth, J., Pearse, G., &amp; Schindler, J. (2023). A systematic review of individual tree crown detection and delineation with convolutional neural networks (CNN). Current Forestry Reports, 9(3), 149–170. https://doi.org/10.1007/s40725-023-00184-3</unstructured_citation>
127+
</citation>
128+
<citation key="weinstein2019">
129+
<article_title>Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks</article_title>
130+
<author>Weinstein</author>
131+
<journal_title>Remote Sensing</journal_title>
132+
<issue>11</issue>
133+
<volume>11</volume>
134+
<doi>10.3390/rs11111309</doi>
135+
<cYear>2019</cYear>
136+
<unstructured_citation>Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., &amp; White, E. (2019). Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sensing, 11(11), 1309. https://doi.org/10.3390/rs11111309</unstructured_citation>
137+
</citation>
138+
<citation key="weinstein2020">
139+
<article_title>Cross-site learning in deep learning RGB tree crown detection</article_title>
140+
<author>Weinstein</author>
141+
<journal_title>Ecological Informatics</journal_title>
142+
<volume>56</volume>
143+
<doi>10.1016/j.ecoinf.2020.101061</doi>
144+
<cYear>2020</cYear>
145+
<unstructured_citation>Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., &amp; White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics, 56, 101061. https://doi.org/10.1016/j.ecoinf.2020.101061</unstructured_citation>
146+
</citation>
147+
<citation key="cong2022">
148+
<article_title>Citrus tree crown segmentation of orchard spraying robot based on RGB-d image and improved mask r-CNN</article_title>
149+
<author>Cong</author>
150+
<journal_title>Applied Sciences</journal_title>
151+
<issue>1</issue>
152+
<volume>13</volume>
153+
<doi>10.3390/app13010164</doi>
154+
<cYear>2022</cYear>
155+
<unstructured_citation>Cong, P., Zhou, J., Li, S., Lv, K., &amp; Feng, H. (2022). Citrus tree crown segmentation of orchard spraying robot based on RGB-d image and improved mask r-CNN. Applied Sciences, 13(1), 164. https://doi.org/10.3390/app13010164</unstructured_citation>
156+
</citation>
157+
<citation key="sun2022">
158+
<article_title>Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework</article_title>
159+
<author>Sun</author>
160+
<journal_title>Frontiers in Plant Science</journal_title>
161+
<volume>13</volume>
162+
<doi>10.3389/fpls.2022.914974</doi>
163+
<cYear>2022</cYear>
164+
<unstructured_citation>Sun, C., Huang, C., Zhang, H., Chen, B., An, F., Wang, L., &amp; Yun, T. (2022). Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework. Frontiers in Plant Science, 13, 914974. https://doi.org/10.3389/fpls.2022.914974</unstructured_citation>
165+
</citation>
166+
<citation key="moussaid2021">
167+
<article_title>Tree crowns segmentation and classification in overlapping orchards based on satellite images and unsupervised learning algorithms</article_title>
168+
<author>Moussaid</author>
169+
<journal_title>Journal of Imaging</journal_title>
170+
<issue>11</issue>
171+
<volume>7</volume>
172+
<doi>10.3390/jimaging7110241</doi>
173+
<cYear>2021</cYear>
174+
<unstructured_citation>Moussaid, A., Fkihi, S. E., &amp; Zennayi, Y. (2021). Tree crowns segmentation and classification in overlapping orchards based on satellite images and unsupervised learning algorithms. Journal of Imaging, 7(11), 241. https://doi.org/10.3390/jimaging7110241</unstructured_citation>
175+
</citation>
176+
<citation key="zheng2024">
177+
<article_title>A review of individual tree crown detection and delineation from optical remote sensing images: Current progress and future</article_title>
178+
<author>Zheng</author>
179+
<journal_title>IEEE Geoscience and Remote Sensing Magazine</journal_title>
180+
<doi>10.1109/MGRS.2024.3479871</doi>
181+
<cYear>2024</cYear>
182+
<unstructured_citation>Zheng, J., Yuan, S., Li, W., Fu, H., Yu, L., &amp; Huang, J. (2024). A review of individual tree crown detection and delineation from optical remote sensing images: Current progress and future. IEEE Geoscience and Remote Sensing Magazine. https://doi.org/10.1109/MGRS.2024.3479871</unstructured_citation>
183+
</citation>
184+
<citation key="tolan2024">
185+
<article_title>Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar</article_title>
186+
<author>Tolan</author>
187+
<journal_title>Remote Sensing of Environment</journal_title>
188+
<volume>300</volume>
189+
<doi>10.1016/j.rse.2023.113888</doi>
190+
<cYear>2024</cYear>
191+
<unstructured_citation>Tolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., &amp; others. (2024). Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment, 300, 113888. https://doi.org/10.1016/j.rse.2023.113888</unstructured_citation>
192+
</citation>
193+
<citation key="fayad2024">
194+
<article_title>Hy-TeC: A hybrid vision transformer model for high-resolution and large-scale mapping of canopy height</article_title>
195+
<author>Fayad</author>
196+
<journal_title>Remote Sensing of Environment</journal_title>
197+
<volume>302</volume>
198+
<doi>10.1016/j.rse.2023.113945</doi>
199+
<cYear>2024</cYear>
200+
<unstructured_citation>Fayad, I., Ciais, P., Schwartz, M., Wigneron, J.-P., Baghdadi, N., Truchis, A. de, d’Aspremont, A., Frappart, F., Saatchi, S., Sean, E., &amp; others. (2024). Hy-TeC: A hybrid vision transformer model for high-resolution and large-scale mapping of canopy height. Remote Sensing of Environment, 302, 113945. https://doi.org/10.1016/j.rse.2023.113945</unstructured_citation>
201+
</citation>
202+
<citation key="pan2024">
203+
<article_title>The enduring world forest carbon sink</article_title>
204+
<author>Pan</author>
205+
<journal_title>Nature</journal_title>
206+
<issue>8021</issue>
207+
<volume>631</volume>
208+
<doi>10.1038/s41586-024-07602-x</doi>
209+
<cYear>2024</cYear>
210+
<unstructured_citation>Pan, Y., Birdsey, R. A., Phillips, O. L., Houghton, R. A., Fang, J., Kauppi, P. E., Keith, H., Kurz, W. A., Ito, A., Lewis, S. L., &amp; others. (2024). The enduring world forest carbon sink. Nature, 631(8021), 563–569. https://doi.org/10.1038/s41586-024-07602-x</unstructured_citation>
211+
</citation>
212+
<citation key="sharma2024">
213+
<article_title>Urban trees’ potential for regulatory services in the urban environment: An exploration of carbon sequestration</article_title>
214+
<author>Sharma</author>
215+
<journal_title>Environmental Monitoring and Assessment</journal_title>
216+
<issue>6</issue>
217+
<volume>196</volume>
218+
<doi>10.1007/s10661-024-12634-x</doi>
219+
<cYear>2024</cYear>
220+
<unstructured_citation>Sharma, S., Hussain, S., Kumar, P., &amp; Singh, A. N. (2024). Urban trees’ potential for regulatory services in the urban environment: An exploration of carbon sequestration. Environmental Monitoring and Assessment, 196(6), 504. https://doi.org/10.1007/s10661-024-12634-x</unstructured_citation>
221+
</citation>
222+
<citation key="taimur_khan_2025">
223+
<article_title>DeepTrees_halle (revision 0c528b9)</article_title>
224+
<author>Taimur Khan</author>
225+
<doi>10.57967/hf/4213</doi>
226+
<cYear>2025</cYear>
227+
<unstructured_citation>Taimur Khan. (2025). DeepTrees_halle (revision 0c528b9). Hugging Face. https://doi.org/10.57967/hf/4213</unstructured_citation>
228+
</citation>
229+
<citation key="wu2022">
230+
<article_title>Entropy-based active learning for object detection with progressive diversity constraint</article_title>
231+
<author>Wu</author>
232+
<doi>10.48550/arXiv.2204.07965</doi>
233+
<cYear>2022</cYear>
234+
<unstructured_citation>Wu, J., Chen, J., &amp; Huang, D. (2022). Entropy-based active learning for object detection with progressive diversity constraint (pp. 9397–9406). https://doi.org/10.48550/arXiv.2204.07965</unstructured_citation>
235+
</citation>
236+
<citation key="PyTorchLightning24">
237+
<article_title>PyTorch Lightning</article_title>
238+
<author>Falcon</author>
239+
<doi>10.5281/zenodo.3828935</doi>
240+
<cYear>2024</cYear>
241+
<unstructured_citation>Falcon, W., &amp; The PyTorch Lightning team. (2024). PyTorch Lightning (Version 2.4). https://doi.org/10.5281/zenodo.3828935</unstructured_citation>
242+
</citation>
243+
<citation key="fao2022">
244+
<article_title>Global forest resources assessment 2022</article_title>
245+
<author>Food</author>
246+
<doi>10.4060/cb9360en</doi>
247+
<cYear>2022</cYear>
248+
<unstructured_citation>Food, &amp; United Nations, A. O. of the. (2022). Global forest resources assessment 2022. https://doi.org/10.4060/cb9360en</unstructured_citation>
249+
</citation>
250+
</citation_list>
251+
</journal_article>
252+
</journal>
253+
</body>
254+
</doi_batch>

‎joss.08056/10.21105.joss.08056.pdf‎

923 KB
Binary file not shown.

0 commit comments

Comments
 (0)