GFM: Building Geospatial Foundation Models via Continual Pretraining

Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response. To help improve the applicability and performance of deep learning models on these geospatial tasks, various works have begun investigating foundation models for this domain. Researchers have explored two prominent approaches for introducing such models in geospatial applications, but both have drawbacks in terms of limited performance benefit or prohibitive training cost. Therefore, in this work, we propose a novel paradigm for building highly effective geospatial foundation models with minimal resource cost and carbon impact. We first construct a compact yet diverse dataset from multiple sources to promote feature diversity, which we term GeoPile. Then, we investigate the potential of continual pretraining from large-scale ImageNet-22k models and propose a multi-objective continual pretraining paradigm, which leverages the strong representations of ImageNet while simultaneously providing the freedom to learn valuable in-domain features. Our approach outperforms previous state-of-the-art geospatial pretraining methods in an extensive evaluation on seven downstream datasets covering various tasks such as change detection, classification, multi-label classification, semantic segmentation, and super-resolution

GroundLink: A Dataset Unifying Human Body Movement and Ground Reaction Dynamics

The physical plausibility of human motions is vital to various applications in fields including but not limited to graphics, animation, robotics, vision, biomechanics, and sports science. While fully simulating human motions with physics is an extreme challenge, we hypothesize that we can treat this complexity as a black box in a data-driven manner if we focus on the ground contact, and have sufficient observations of physics and human activities in the real world. To prove our hypothesis, we present GroundLink, a unified dataset comprised of captured ground reaction force (GRF) and center of pressure (CoP) synchronized to standard kinematic motion captures. GRF and CoP of GroundLink are not simulated but captured at high temporal resolution using force platforms embedded in the ground for uncompromising measurement accuracy. This dataset contains 368 processed motion trials (~1.59M recorded frames) with 19 different movements including locomotion and weight-shifting actions such as tennis swings to signify the importance of capturing physics paired with kinematics. GroundLinkNet, our benchmark neural network model trained with GroundLink, supports our hypothesis by predicting GRFs and CoPs accurately and plausibly on unseen motions from various sources. The dataset, code, and benchmark models are made public for further research on various downstream tasks leveraging the rich physics information at https://csr.bu.edu/groundlink/

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions. To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop PreDiff, a conditional latent diffusion model capable of probabilistic forecasts. 2) We incorporate an explicit knowledge control mechanism to align forecasts with domain-specific physical constraints. This is achieved by estimating the deviation from imposed constraints at each denoising step and adjusting the transition distribution accordingly. We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of PreDiff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility.Comment: Technical repor

A genome-wide association study identifies FSHR rs2300441 associated with follicle-stimulating hormone levels

Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play critical roles in female reproduction, while the underlying genetic basis is poorly understood. Genome-wide association studies (GWASs) of FSH and LH levels were conducted in 2590 Chinese females including 1882 polycystic ovary syndrome (PCOS) cases and 708 controls. GWAS for FSH level identified multiple variants at FSHR showing genome-wide significance with the top variant (rs2300441) located in the intron of FSHR. The A allele of rs2300441 led to a reduced level of FSH in the PCOS group (β = −.43, P = 6.70 × 10−14) as well as in the control group (β = −.35, P = 6.52 × 10−4). In the combined sample, this association was enhanced after adjusting for the PCOS status (before: β = −.38, P = 1.77 × 10−13; after: β = −.42, P = 3.33 × 10−16), suggesting the genetic effect is independent of the PCOS status. The rs2300441 explained sevenfold higher proportion of the FSH variance than the total variance explained by the two previously reported FSHR missense variants (rs2300441 R2 = 1.40% vs rs6166 R2 = 0.17%, rs6165 R2 = 0.03%). GWAS for LH did not identify any genome-wide significant associations. In conclusion, we identified genome-wide significant association between variants in FSHR and circulating FSH first, with the top associated variant rs2300441 might be a primary contributor at the population level

Constitutive analysis for hot deformation behaviour of novel bimetal consisting of pearlitic steel and low carbon steel

To understand the high temperature flow behaviour of a novel pearlitic steel (PS) and low carbon steel (LCS) bimetal, hot compression tests in a wide range of temperature and strain rate were conducted on a Gleeble 3500 thermo mechanical simulator, and the constitutive model was developed based on the experimental data. The measured true stress-strain curves exhibited three types of variation patterns, which are (i) a plateau type, (ii) single peak type and (iii) multi peaks type. These patterns well displayed the effects of the deformation temperature, strain rate and plastic strain on the flow behaviour of the bimetal. By incorporating the Zener-Hollomon parameter and material parameter functions of α(ε), n(ε), Q(ε) and A(ε) into Arrhenius-type constitutive equation, the flow stress values predicted by the proposed model show a good agreement with experimental results by the evidence of reproducing true stress-strain curves accurately, high value of correlation coefficient (R=0.9873) and low value of average absolute relative error (AARE=4.81%). The proposed constitutive equation can be used to realise numerical simulation and determine processing parameters during hot-working of the PS/LCS bimetal

Additional file 1 of Ectopic intrapulmonary thyroid masquerading as metastatic carcinoma of the lung: a rare case scenario

Supplementary Material