[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3077":3,"related-3077":61},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":40,"slug":41,"view_count":15,"doi":42,"paper":43,"created_at":60},3077,"A Computer Vision Survey of the Northern San Juan","https:\u002F\u002Fdoi.org\u002F10.1017\u002Faaq.2026.10223","Abstract As computer vision becomes more popular in archaeology, it is imperative to develop best practices that balance the interpretive and analytically critical tendencies of manual survey with the systemization and efficiency promised by machine learning. Using a case study from the northern US Southwest, we demonstrate an iterative image classification approach that reflects the methodological processes that play out in field research and preserves the role of humans as nuanced decision-makers. We develop a model to identify ancestral Pueblo residential sites and use it to survey an area larger than 16,000 km 2 . The semiautomated survey identified 4,905 likely archaeological features, marking one of the largest remote archaeological surveys in North America. Results not only emphasize the value of integrated computer vision for archaeological survey and site prediction but also demonstrate a research design that capitalizes on the computational value of computer vision while maintaining active engagement by the researcher.","摘要 随着计算机视觉在考古学中日益普及，亟需制定最佳实践，以平衡人工调查的解释性与分析批判性倾向同机器学习所承诺的系统化与效率之间的关系。通过美国西南部北部的一个案例研究，我们展示了一种迭代式图像分类方法，该方法反映了田野研究中实际展开的方法论过程，并保留了人类作为细致决策者的角色。我们开发了一个模型来识别祖先普韦布洛（Pueblo）居住遗址，并用其调查了超过16,000平方公里的区域。这种半自动化调查识别出4,905个可能的考古特征，是北美规模最大的远程考古调查之一。研究结果不仅强调了集成计算机视觉在考古调查与遗址预测中的价值，还展示了一种研究设计，该设计在利用计算机视觉计算价值的同时，保持了研究者的积极参与。",null,"American Antiquity","2026-09-18T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"考古学计算机视觉调查论文，与三农、农业信息化、智慧农业主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":39},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":38},"I2",17,true,[33],"computer vision",[35,36,37,33],"机器学习","计算机视觉","machine learning",[],"农业相关性需要复核",[],"AComputerVisionSurveyoftheNorthernSanJua-3077","10.1017\u002Faaq.2026.10223",{"doi":42,"openalex_id":44,"authors":45,"venue":10,"cited_by_count":15,"oa_url":52,"card":53,"direction":57,"ingested_from":59},"W7213548823",[46,49],{"name":47,"orcid":48},"Sean Field","https:\u002F\u002Forcid.org\u002F0000-0002-3144-5796",{"name":50,"orcid":51},"L. A. Dean","https:\u002F\u002Forcid.org\u002F0009-0000-7038-8176","https:\u002F\u002Fwww.cambridge.org\u002Fcore\u002Fservices\u002Faop-cambridge-core\u002Fcontent\u002Fview\u002F6BE98BA78AE0BFF7A688E579B669BB8F\u002FS0002731626102236a.pdf\u002Fdiv-class-title-a-computer-vision-survey-of-the-northern-san-juan-div.pdf",{"tldr":54,"method":55,"finding":56,"direction":57,"opportunity":58},"用迭代图像分类模型识别祖先普韦布洛居住遗址，半自动调查超1.6万平方公里区域。","迭代图像分类、计算机视觉模型，遥感影像，考古遗址识别。","识别出4905个可能考古特征，是北美最大远程考古调查之一。","农业遥感与作物表型","可借鉴其迭代人机协同方法，用于农业遗址或耕地遥感识别与制图。","openalex","2026-09-21T23:30:25.985394Z",{"total":15,"page":62,"page_size":62,"items":63},1,[]]