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Late Eocene artiodactyl and creodont tracks have been reported from the Jianchuan Basin, southeastern Tibetan Plateau, representing the first and the oldest reported mammal tracks in Yunnan Province, China. Sedimentary facies analysis indicates that tracks from the upper Baoxiangsi Formation and the lower Shuanghe Formation occurred in lake shore and flood plain environments, respectively. Based on the morphological analysis, the artiodactyl tracks from the Baoxiangsi Formation are attributed to Anoplotheriipus zeuctus. Combined with fossil records and morphological characteristics, the artiodactyl and creodont tracks were probably produced by entelodonts and mesonychids, respectively. The superimposition of artiodactyl tracks with varying sizes indicates coexistence of juveniles and adults as well as their gregarious behaviour. The co-occurrence of artiodactyl tracks and creodont tracks suggests the predator–prey relationship and ecological chain during the late Eocene. The presence of mammal tracks in the Jianchuan Basin coincides with the climate change from dry to wet in the late Eocene, implying the climatic and ecological effects of the uplift of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]