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Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)? We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.A national compilation of landslide inventories by the U.S. Geological Survey (USGS) was used to develop database for this study. We focused on space-visible landslides within four ecoregions. These landslides were manually identified and labeled using high-resolution satellite images from Google Earth. The database contains 496 labeled and georeferenced pre-event/postevent images pairs, which corresponding to 1,918 landslide records in the USGS landslide inventory. Using the TSMU framework, we conduct extensive experiments on four ecoregions in the United States to address the aforementioned questions.

The frame of the cotton picker is exposed to complex and varying loads during its operation. Therefore, conducting research on the vibration characteristics of the frame is crucial. In this study, vibration tests were conducted on the main vibration sources in a cotton picker at several measuring points on the frame. An accelerometer sensor was utilized to collect the signals. Fourier analysis was applied to analyze the vibration sources, encompassing the excitation frequency and the vibration source-coupled excitation frequency. Modal tests were also conducted to validate the finite element model and determine the natural frequencies of the frame. The results showed that the natural frequencies of the frame, specifically the third-order, fourth-order, and sixth-order frequencies, were comparable to the vibration source-coupled excitation frequencies. To prevent frame resonance, the response surface method was used to optimize the frame. Based on the MOGA algorithm, scheme 4 was identified as the optimal design. Furthermore, fatigue life calculations were carried out to optimize the parts with short lifespans on the frame, thereby enhancing the working performance.

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