{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:39:15Z","timestamp":1786120755368,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3818482","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:43Z","timestamp":1786036543000},"page":"7209-7220","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Smallholder Agricultural Landscape Understanding at a National Scale"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8424-1033","authenticated-orcid":false,"given":"Radhika","family":"Dua","sequence":"first","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3908-3579","authenticated-orcid":false,"given":"Aditi","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5658-0724","authenticated-orcid":false,"given":"Aishwarya","family":"Jayagopal","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4029-4562","authenticated-orcid":false,"given":"Depanshu","family":"Sani","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9064-0076","authenticated-orcid":false,"given":"Alex","family":"Wilson","sequence":"additional","affiliation":[{"name":"Google, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1969-8391","authenticated-orcid":false,"given":"Hoang","family":"Tran","sequence":"additional","affiliation":[{"name":"Google, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7987-4200","authenticated-orcid":false,"given":"Ishan","family":"Deshpande","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7715-7650","authenticated-orcid":false,"given":"Bogdan","family":"Floristean","sequence":"additional","affiliation":[{"name":"Google, Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2267-8811","authenticated-orcid":false,"given":"Neelabh","family":"Goyal","sequence":"additional","affiliation":[{"name":"Google, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3730-6700","authenticated-orcid":false,"given":"Ramya","family":"Cheruvu","sequence":"additional","affiliation":[{"name":"Google, Mountain View, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0461-0730","authenticated-orcid":false,"given":"Vishal","family":"Batchu","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1932-412X","authenticated-orcid":false,"given":"Yan","family":"Mayster","sequence":"additional","affiliation":[{"name":"Google, Boulder, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9379-8014","authenticated-orcid":false,"given":"Gaurav","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4604-2328","authenticated-orcid":false,"given":"Alok","family":"Talekar","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6748-6864","authenticated-orcid":false,"given":"Vaibhav","family":"Rajan","sequence":"additional","affiliation":[{"name":"Google DeepMind, Bengaluru, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"https:\/\/agristack.gov.in\/ Accessed","author":"Stack Agri","year":"2026","unstructured":"AgriStack. 2026. Agri Stack. https:\/\/agristack.gov.in\/ Accessed: 28 May 2026."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00046"},{"key":"e_1_3_2_2_3_1","volume-title":"A metric for polygon comparison and building extraction evaluation","author":"Avbelj Janja","year":"2014","unstructured":"Janja Avbelj, Rupert M\u00fcller, and Richard Bamler. 2014. A metric for polygon comparison and building extraction evaluation. IEEE Geoscience and Remote Sensing Letters, V12, 1 (2014), 170-174."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"R.G.V. Bramley and J. Ouzman. 2019. Farmer attitudes to the use of sensors and automation in fertilizer decision-making: Nitrogen fertilization in the Australian grains sector. Precision Agriculture V20 (2019) 157-175.","DOI":"10.1007\/s11119-018-9589-y"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Y. Cai K. Guan J. Peng S. Wang C. Seifert B. Wardlow and Z. Li. 2018. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment V210 (2018) 35-47.","DOI":"10.1016\/j.rse.2018.02.045"},{"key":"e_1_3_2_2_6_1","volume-title":"https:\/\/carbonfarm.tech\/ Accessed","year":"2026","unstructured":"CarbonFarm. 2026. CarbonFarm. https:\/\/carbonfarm.tech\/ Accessed: 28 May 2026."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"e_1_3_2_2_9_1","volume-title":"V17, 10","author":"Chuang Yuelong","year":"2023","unstructured":"Yuelong Chuang, Shiqing Zhang, and Xiaoming Zhao. 2023. Deep learning-based panoptic segmentation: Recent advances and perspectives. IET Image Processing, V17, 10 (2023), 2807-2828."},{"key":"e_1_3_2_2_10_1","volume-title":"V6","author":"Cofie Olufunke","year":"2015","unstructured":"Olufunke Cofie and Tilahun Amede. 2015. Water management for sustainable agricultural intensification and smallholder resilience in sub-Saharan Africa. Water Resources and Rural Development, V6 (2015), 3-11."},{"key":"e_1_3_2_2_11_1","volume-title":"Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery. Advances in Neural Information Processing Systems, V35","author":"Cong Yezhen","year":"2022","unstructured":"Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, and Stefano Ermon. 2022. Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery. Advances in Neural Information Processing Systems, V35 (2022), 197-211."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14040965"},{"key":"e_1_3_2_2_13_1","unstructured":"Cooperation Department of Agriculture and Farmers Welfare. 2016. All India Report on Agriculture Census 2015-16. Technical Report. Ministry of Agriculture and Farmers Welfare Government of India. https:\/\/agcensus.da.gov.in\/document\/agcen1516\/ac_1516_report_final-220221.pdf"},{"key":"e_1_3_2_2_14_1","volume-title":"V227","author":"Donohue Randall J","year":"2018","unstructured":"Randall J Donohue, Roger A Lawes, Gonzalo Mata, David Gobbett, and Jackie Ouzman. 2018. Towards a national, remote-sensing-based model for predicting field-scale crop yield. Field Crops Research, V227 (2018), 79-90."},{"key":"e_1_3_2_2_15_1","volume-title":"https:\/\/krishi-dss.gov.in\/krishi-dss\/ Accessed","author":"Krishi DSS.","year":"2026","unstructured":"Krishi DSS. 2026. Krishi DSS. https:\/\/krishi-dss.gov.in\/krishi-dss\/ Accessed: 28 May 2026."},{"key":"e_1_3_2_2_16_1","volume-title":"Panoptic segmentation: A review. arXiv preprint arXiv:2111.10250","author":"Elharrouss Omar","year":"2021","unstructured":"Omar Elharrouss, Somaya Al-Maadeed, Nandhini Subramanian, Najmath Ottakath, Noor Almaadeed, and Yassine Himeur. 2021. Panoptic segmentation: A review. arXiv preprint arXiv:2111.10250 (2021)."},{"key":"e_1_3_2_2_17_1","unstructured":"Eric Veach et al. [n.d.]. S2Geometry. http:\/\/s2geometry.io\/ Accessed: 16 August 2024."},{"key":"e_1_3_2_2_18_1","volume-title":"Emissions Totals","author":"FAO.","unstructured":"FAO. 2023. FAOSTAT Climate Change: Agrifood systems emissions, Emissions Totals. http:\/\/www.fao.org\/faostat\/en\/#data\/GT Accessed: 2024-09-17."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.4060\/cc3017en"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00073"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43016-023-00698-w"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"A. Garcia-Pedrero M. Lillo-Saavedra D. Rodriguez-Esparragon and C. Gonzalo-Martin. 2019. Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System. IEEE Access V7 (2019) 158223-158236.","DOI":"10.1109\/ACCESS.2019.2950371"},{"key":"e_1_3_2_2_23_1","volume-title":"https:\/\/maps.google.com\/pluscodes\/ Accessed","author":"Codes Plus","year":"2024","unstructured":"Google. 2014. Plus Codes. https:\/\/maps.google.com\/pluscodes\/ Accessed: 16 August 2024."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_2_25_1","unstructured":"F. Hodgman. [n.d.]. A Manual of Land Surveying comprising an elementary course of practice with instruments and a treatise upon the survey of public and private lands. https:\/\/archive.org\/details\/manualoflandsurv00hodgrich\/page\/n7\/mode\/2up Accessed: 17 September 2024."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"M. Jain A.K. Srivastava R.K. Joon A. McDonald K. Royal M.C. Lisaius and D.B. Lobell. 2016. Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sensing V8 10 (2016) 860.","DOI":"10.3390\/rs8100860"},{"key":"e_1_3_2_2_27_1","volume-title":"V228","author":"Kang Y.","year":"2019","unstructured":"Y. Kang and M. \u00d6zdo\u011fan. 2019. Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach. Remote Sensing of Environment, V228 (2019), 144-163."},{"key":"e_1_3_2_2_28_1","volume-title":"Proceedings of the AAAI Workshop on AI to Accelerate Science and Engineering.","author":"Kerner Hannah","year":"2023","unstructured":"Hannah Kerner, Saketh Sundar, and Mathan Satish. 2023. Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels. In Proceedings of the AAAI Workshop on AI to Accelerate Science and Engineering."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00656"},{"key":"e_1_3_2_2_30_1","volume-title":"https:\/\/water.karnataka.gov.in\/ Accessed","author":"KWRIS.","year":"2026","unstructured":"KWRIS. 2026. KWRIS. https:\/\/water.karnataka.gov.in\/ Accessed: 28 May 2026."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcc.301"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"M. Lesiv J.C. Laso Bayas L. See M. Duerauer D. Dahlia N. Durando R. Hazarika P. Kumar Sahariah M. Vakolyuk and V. Blyshchyk. 2019. Estimating the Global Distribution of Field Size Using Crowdsourcing. Global Change Biology V25 (2019) 174-186.","DOI":"10.1111\/gcb.14492"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_2_34_1","first-page":"740","volume-title":"Switzerland","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Computer vision-ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13. Springer, 740-755."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"L. Ma Y. Liu X. Zhang Y. Ye G. Yin and B.A. Johnson. 2019. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing V152 (2019) 166-177.","DOI":"10.1016\/j.isprsjprs.2019.04.015"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/w13243627"},{"key":"e_1_3_2_2_37_1","volume-title":"Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data. International Journal of Remote Sensing, V42, 4","author":"Marvaniya S.","year":"2021","unstructured":"S. Marvaniya, U. Devi, J. Hazra, S. Mujumdar, and N. Gupta. 2021. Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data. International Journal of Remote Sensing, V42, 4 (2021), 1512-1534."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"K.M. Masoud C. Persello and V.A. Tolpekin. 2020. Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks. Remote Sensing V12 1 (2020) 59.","DOI":"10.3390\/rs12010059"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"A.E. Maxwell T.A. Warner and F. Fang. 2018. Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing V39 9 (2018) 2784-2817.","DOI":"10.1080\/01431161.2018.1433343"},{"key":"e_1_3_2_2_40_1","unstructured":"Yan Mayster and Brian Shucker. 2019. Generation of surface maps to improve navigation. http:\/\/patents.google.com\/patent\/WO2020219028A1\/en"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14133046"},{"key":"e_1_3_2_2_42_1","unstructured":"Ministry of Agriculture and Farmers Welfare. 2024. Categorisation of Farmers. https:\/\/www.pib.gov.in\/PressReleasePage.aspx?PRID=2085181."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"N.D. Mueller J.S. Gerber M. Johnston D.K. Ray N. Ramankutty and J.A. Foley. 2012. Closing Yield Gaps through Nutrient and Water Management. Nature V490 7419 (2012) 254-257.","DOI":"10.1038\/nature11420"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1787\/b14de474-en"},{"key":"e_1_3_2_2_45_1","volume-title":"Assam Land Records. https:\/\/landrevenue.assam.gov.in\/sites\/default\/files\/swf_utility_folder\/departments\/revenue_com_oid_6\/menu\/document\/assam_land_record_manual-compressed.pdf Accessed","author":"Assam Government","year":"2024","unstructured":"Government of Assam. [n.d.]. Assam Land Records. https:\/\/landrevenue.assam.gov.in\/sites\/default\/files\/swf_utility_folder\/departments\/revenue_com_oid_6\/menu\/document\/assam_land_record_manual-compressed.pdf Accessed: 29 August 2024."},{"key":"e_1_3_2_2_46_1","volume-title":"MH Land Records. https:\/\/dspace.gipe.ac.in\/xmlui\/bitstream\/handle\/1\/38474\/GIPE-159571.pdf?sequence=3&isAllowed=y Accessed","author":"Maharashtra Government","year":"2024","unstructured":"Government of Maharashtra. [n.d.]. MH Land Records. https:\/\/dspace.gipe.ac.in\/xmlui\/bitstream\/handle\/1\/38474\/GIPE-159571.pdf?sequence=3&isAllowed=y Accessed: 29 August 2024."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.9734\/ijecc\/2023\/v13i113615"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2023.3323095"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2021.107385"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"N.E. Rada and K.O. Fuglie. 2019. New perspectives on farm size and productivity. Food Policy V84 (2019) 147-152.","DOI":"10.1016\/j.foodpol.2018.03.015"},{"key":"e_1_3_2_2_51_1","unstructured":"Nikhila Ravi Valentin Gabeur Yuan-Ting Hu Ronghang Hu Chaitanya Ryali Tengyu Ma Haitham Khedr Roman R\u00e4dle Chloe Rolland Laura Gustafson et al. 2024. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00378"},{"key":"e_1_3_2_2_53_1","first-page":"234","volume-title":"Munich","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer, 234-241."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0812540106"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","unstructured":"M. \u0160\u00e1lek V. Hula M. Kipson R. Da\u0148kov\u00e1 J. Niedobov\u00e1 and A. Gamero. 2018. Bringing diversity back to agriculture: Smaller fields and non-crop elements enhance biodiversity in intensively managed arable farmlands. Ecological Indicators V90 (2018) 65-73.","DOI":"10.1016\/j.ecolind.2018.03.001"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"crossref","unstructured":"L.H. Samberg J.S. Gerber N. Ramankutty M. Herrero and P.C. West. 2016. Subnational Distribution of Average Farm Size and Smallholder Contributions to Global Food Production. Environmental Research Letters V11 12 (2016) 124010.","DOI":"10.1088\/1748-9326\/11\/12\/124010"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"M. Segoli and J.A. Rosenheim. 2012. Should increasing the field size of monocultural crops be expected to exacerbate pest damage? Agriculture Ecosystems & Environment V150 (2012) 38-44.","DOI":"10.1016\/j.agee.2012.01.010"},{"key":"e_1_3_2_2_58_1","unstructured":"Oriane Sim\u00e9oni Huy V Vo Maximilian Seitzer Federico Baldassarre Maxime Oquab Cijo Jose Vasil Khalidov Marc Szafraniec Seungeun Yi Micha\u00ebl Ramamonjisoa et al. 2025. Dinov3. arXiv preprint arXiv:2508.10104 (2025)."},{"key":"e_1_3_2_2_59_1","volume-title":"V196, 11","author":"Su Qiong","year":"2024","unstructured":"Qiong Su and Vijay P Singh. 2024. Advancing irrigation management: integrating technology and sustainability to address global food security. Environmental monitoring and assessment, V196, 11 (2024), 1018."},{"key":"e_1_3_2_2_60_1","unstructured":"Gerard Sylvester et al. 2015. Success stories on information and communication technologies for agriculture and rural development. (2015)."},{"key":"e_1_3_2_2_61_1","volume-title":"Benedikt Blumenstiel, Rinki Ghosal, Pedro Henrique De Oliveira, Joao Lucas de Sousa Almeida, Rocco Sedona, Yanghui Kang, et al.","author":"Szwarcman Daniela","year":"2025","unstructured":"Daniela Szwarcman, Sujit Roy, Paolo Fraccaro, Orsteinn El\u00ed G\u00edslason, Benedikt Blumenstiel, Rinki Ghosal, Pedro Henrique De Oliveira, Joao Lucas de Sousa Almeida, Rocco Sedona, Yanghui Kang, et al., 2025. Prithvi-eo-2.0: A versatile multi-temporal foundation model for earth observation applications. IEEE Transactions on Geoscience and Remote Sensing (2025)."},{"key":"e_1_3_2_2_62_1","unstructured":"Teamup. 2024. Teamup. https:\/\/teamup.ink\/."},{"key":"e_1_3_2_2_63_1","volume-title":"https:\/\/terrastack.in\/ Accessed","year":"2026","unstructured":"Terrastack. 2026. Terrastack. https:\/\/terrastack.in\/ Accessed: 28 May 2026."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.87344"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"crossref","unstructured":"M.P. Wagner and N. Oppelt. 2020. Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours. Remote Sensing V12 7 (2020) 1205.","DOI":"10.3390\/rs12071205"},{"key":"e_1_3_2_2_66_1","volume-title":"V245","author":"Waldner Fran\u00e7ois","year":"2020","unstructured":"Fran\u00e7ois Waldner and Foivos I Diakogiannis. 2020. Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network. Remote sensing of environment, V245 (2020), 111741."},{"key":"e_1_3_2_2_67_1","volume-title":"V13, 11","author":"Waldner Fran\u00e7ois","year":"2021","unstructured":"Fran\u00e7ois Waldner, Foivos I Diakogiannis, Kathryn Batchelor, Michael Ciccotosto-Camp, Elizabeth Cooper-Williams, Chris Herrmann, Gonzalo Mata, and Andrew Toovey. 2021. Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images. Remote sensing, V13, 11 (2021), 2197."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14225738"},{"key":"e_1_3_2_2_69_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR).","author":"Xu Zongzhe","year":"2025","unstructured":"Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, and Mikhail Khodak. 2025. Specialized foundation models struggle to beat supervised baselines. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"crossref","unstructured":"L. Yan and D. Roy. 2016. Conterminous United States crop field size quantification from multi-temporal Landsat data. Remote Sensing of Environment V172 (2016) 67-86.","DOI":"10.1016\/j.rse.2015.10.034"},{"key":"e_1_3_2_2_71_1","volume-title":"K-net: Towards unified image segmentation. Advances in Neural Information Processing Systems, V34","author":"Zhang Wenwei","year":"2021","unstructured":"Wenwei Zhang, Jiangmiao Pang, Kai Chen, and Chen Change Loy. 2021. K-net: Towards unified image segmentation. Advances in Neural Information Processing Systems, V34 (2021), 10326-10338."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:09:07Z","timestamp":1786064947000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3818482"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":71,"alternative-id":["10.1145\/3770855.3818482","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3818482","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}