[1] Xiangdong WANG, Xiuying SHAO. Remote sensing—The third eye of archaeological researching in modern. Journal of Taiyuan Teachers（Natural Science Edition）, 3, 68-71(2004).

[2] Ning YIN, Changlin WANG. Application of remote sensing techniques in archaeological prospecting. Remote Sensing Technology and Application, 18, 258-262(2003).

[4] Kelong TAN, Lin YANG, Riping ZHOU et al. Research on hyperspectral remote sensing for archaeology in Shenheyuan， Xi'an. Journal of Basic Science and Engineering, 17, 675-682(2009).

[5] Riping ZHOU. Study on comprehensive remote sensing archaeological method using hyperspetral data—A case study of Chang'an - Huyi area. Journal of Anhui University of Science and Technology （Natural Science）, 39, 73-77(2019).

[6] Lijun YU, Yueping NIE, Lin YANG et al. A comprehensive spatial archaeological study of the Koyuk Shahri ancient city in Xinjiang. Remote Sensing Technology and Application, 35, 255-266(2020).

[7] Wenjun ZHANG. A study on the detection of typical sites in the central plains based on multi-source remote sensing NDVI timeseries reconstruction(2015).

[8] W DEADMAN, N AL-JAHWARI. Hafit tombs in Ash-Sharqiyah， Oman： Assessing the accuracy and precision of Google Earth remote-sensing survey and analysing their distribution in the landscape. Arabian Archaeology and Epigraphy, 27, 19-30(2016).

[9] T BALZ, G CASPARI, B FU et al. Discernibility of burial mounds in high-resolution X-band SAR images for archaeological prospections in the Altai mountains. Remote Sensing, 8, 817(2016).

[10] Nan SHANG, Lijun YU, Yueping NIE. Study of distribution cha-racteristics of archaeological sites in Fenhe River basin based on logistic regression. Science of Surveying and Mapping, 40, 47-52(2015).

[11] A F NSANZIYERA, H LECHGAR et al. Remote-sensing data-based archaeological predictive model （apm） for archaeological mapping in desert area， south Morocco. Comptes Rendus Geoscience, 350, 319-330(2018).

[12] Ruichan WU, Shu GAN, Lijun YU et al. The study of predictive distribution of Pakistani sites based on maxent model. Science of Surveying and Mapping, 43, 96-103(2021).

[13] MATAS G. Dunes. modelling funerary monument construction in remote arid landscapes using spaceborne stereo imagery. Journal of Archaeological Science, 156(2023).

[14] E J LAUGIER, N ABDULLATIF, C GLATZ. Embedding the remote sensing monitoring of archaeological site damage at the local level： results from the “Archaeological practice and heritage protection in the Kurdistan Region of Iraq” project. Public Library of Science ONE, 17(2022).

[15] G CASPARI, P CRESPO. Convolutional neural networks for archaeological site detection – finding “princely” tombs. Journal of Archaeological Science, 110, 104998(2019).

[16] F CHEN, R ZHOU, T VAN DE VOORDE et al. Automatic detection of burial mounds（Kurgans） in the Altai mountains. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 217-237(2021).

[17] W B VERSCHOOF-VAN DER VAART, K LAMBERS. Learning to look at LiDAR： the use of R-CNN in the automated detection of archaeological objects in LiDAR data from the Netherlands. Journal of Computer Applications in Archaeology, 2, 31-40(2019).

[18] I BERGANZO-BESGA, ORENGO H.A, F LUMBRERAS et al. Hybrid MSRM-based deep learning and multitemporal sentinel 2-based machine learning algorithm detects near 10k archaeological tumuli in north-western Iberia. Remote Sensing, 13, 4181(2021).

[19] A BONHAGE, M ELTAHER, T RAAB et al. A modified mask region-based convolutional neural network approach for the automated detection of archaeological sites on high-resolution light detection and ranging-derived digital elevation models in the north German lowland. Archaeological Prospection, 28, 177-186(2021).

[20] D CANEDO, J FONTE, L G SECO et al. Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence. IEEE Access, 65608-65619(2023).

[21] S REN, K HE, R GIRSHICK et al. Faster R-CNN： towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[22] Z W CAI, N VASCONCELOS. Cascade R-CNN：Delving into high quality object detection, 6154-6162(2018).

[23] C Y WANG, A BOCHKOVSKIY, LIAO H-Y MARK. YOLOv7： Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 7464-7475(2023).

[24] B D ZENG. The discovery and study of sarcop-hagus tombs in the Altai region of Xinjiang. Silk Road Vision, 53-56(2017).

[25] Zhuoya BAHETI. Study on the patterns of gold products in prehistoric times of Altai， Xinjiang(2017).

[26] Jianjun YU, Yuesheng LIU, Xiao HE. Glean the green glistening “golden mountain”- excavation of the East Taldi cemetery in Habahe county， Altai. Civilization, 2013, 88-101.

[27] Xiaobing JIA. Preliminary analysis of ancient burials in Hot Spring county， Xinjiang. Northern Cultural Relics, 67-80(2020).

[28] Siwen LIU. Characteristics of stone burials from the early iron age in Altai， Xinjiang. Journal of Ningxia University （Humanities & Social Sciences Edition）, 38, 63-67(2016).

[29] W KIM, Y P NIE, J F ZHU et al. Local orientation based detection of circular soil marks of ancient graves by GA. Journal of Remote Sensing, 17, 671-678(2013).

[30] Miaomiao FU, Miaolei DENG, Dexian ZHANG. Survey on deep neural network image target detection algorithms. Computer Systems and Applications, 31, 35-45(2022).

[31] Kequan LI, Yan CHEN, Jiachen LIU et al. Survey of deep learning-based object detection algorithms. Computer Engineering, 48, 1-12(2022).

[32] W T WU, Han LIU, L L LI et al. Application of local fully convolutional neural network combined with YOLOv5 algorithm in small target detection of remote sensing image. PloS ONE, 16(2021).

[33] Xikun WANG, Hongxu JIANG, Keyu LIN. Remote sensing image ship detection based on modified YOLO algorithm. Journal of Beijing University of Aeronautics and Astronautics, 46, 1184-1191(2020).

[34] Jinchuan WANG, Xicheng TAN, Zhaohai WANG et al. Faster R-CNN deep learning network based object recognition of remote sensing image. Journal of Geo-information Science, 20, 1500-1508(2018).

[35] Fu XIE, Dingju ZHU. Survey on deep learning object detection. Computer Systems and Applications, 31, 1-12(2022).

[36] Xu CHENG, Chen SONG, Jingang SHI et al. A survey of generic object detection methods based on deep learning. Acta Electronica Sinica, 49, 1428-1438(2021).

[37] R GIRSHICK, J DONAHUE, T DARRELL et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 580-587(2014).

[38] R GIRSHICK. Fast R-CNN, 1440-1448(2015).

[39] J REDMON, S DIVVALA, R GIRSHICK et al. You only look once：Unified，real-time object detection, 779-788(2016).

[40] W LIU, D ANGUELOV, D ERHAN et al. SSD： single shot multibox detector, 21-27(2016).

[41] Wentao ZHU, Xianchao LAN, Huanlin LUO et al. Remote sensing aircraft target detection based on improved Faster R-CNN. Computer Science, 49, 378-383(2022).

[42] Guozheng XU, Yue ZHOU, Bin DONG et al. Traffic signs recognition based on improved Cascade R-CNN. Transducer and Microsystem Technologies, 40, 142-145，153(2021).

[43] Yonggang LI, Weigang ZHU, Qiongnan HUANG et al. Near-shore ship target detection with SAR images in complex background. Systems Engineering and Electronics Technology, 44, 3096-3103(2022).

[44] Lei GUO, Qiulong WANG, Wei XUE et al. A small object detection algorithm based on improved YOLOv5. Journal of University of Electronic Science and Technology of China, 51, 251-258(2022).

[45] Xiandong TAN, Hui PENG. Improved YOLOv5 ship target detection in SAR image. Computer Engineering and Applications, 58, 247-254(2022).

[46] Linglong QI, Jianling GAO. Small object detection based on improved YOLOv7. Computer Engineering, 49, 41-48(2003).

[47] Yining CAO, Chao LI, Yakun PENG. Night pedestrian detecction algorithm based on improved YOLOv7. Changjiang Information and Communication, 35, 57-60(2022).

[48] Z GE, S T LIU, F WANG et al. YOLOX：Exceeding YOLO series in 2021. arXiv：2107, 2021(08430).

[49] G CASPARI. Assessing looting from space： the destruction of early iron age burials in northern Xinjiang. Heritage, 1, 320-327(2018).

[50] W KIM. Research on the detection of suspected ancient burial features based on high-resolution panchromatic satellite images：A case study of northern Xinjiang region，China(2012).

[51] R LASAPONARA, N MASINI. Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. Journal of Archaeological Science, 34, 214-221(2006).

[52] A AGAPIOU, D G.HADJIMITSIS, A SARRIS et al. Optimum temporal and spectral window for monitoring crop marks over archaeological remains in the mediterranean region. Journal of Archaeological Science, 40(2013).

[53] Jianjun YU. Excavation results of the cemeteries of Galangash and Habaikhan in Altai， Xinjiang. The Western Regions Studies, 130-131(2013).

[54] Jianjun YU. Conservation of cultural relics at field archaeological excavation sites and their subsequent development in Habahe county， Altai region， Xinjiang, 179-187(2015).

[55] Yurong LIAO, Haining WANG, Cunbao LIN et al. Research progress of deep learning-based object detection of optical remote sensing image. Journal of Communication, 43, 190-203(2022).

[56] Jinchao XING, Guangzhen PAN. Research on improved YOLOv5 sign language recognition algorithm. Computer Engineering and Application, 58, 194-203(2022).