FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems

• URL: http://arxiv.org/abs/2311.10255v2
• Date: Sat, 20 Apr 2024 00:15:04 GMT
• Title: FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
• Authors: Shiyuan Luo, Juntong Ni, Shengyu Chen, Runlong Yu, Yiqun Xie, Licheng Liu, Zhenong Jin, Huaxiu Yao, Xiaowei Jia,
• Abstract summary: FREE maps available environmental data into a text space and then converts the traditional predictive modeling task in environmental science to the semantic recognition problem. When used for long-term prediction, FREE has the flexibility to incorporate newly collected observations to enhance future prediction. The efficacy of FREE is evaluated in the context of two societally important real-world applications, predicting stream water temperature in the Delaware River Basin and predicting annual corn yield in Illinois and Iowa.
• Score: 28.166089112650926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling environmental ecosystems is critical for the sustainability of our planet, but is extremely challenging due to the complex underlying processes driven by interactions amongst a large number of physical variables. As many variables are difficult to measure at large scales, existing works often utilize a combination of observable features and locally available measurements or modeled values as input to build models for a specific study region and time period. This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships amongst various environmental data over space and time? In this paper, we introduce a new framework, FREE, which maps available environmental data into a text space and then converts the traditional predictive modeling task in environmental science to the semantic recognition problem. The proposed FREE framework leverages recent advances in Large Language Models (LLMs) to supplement the original input features with natural language descriptions. This facilitates capturing the data semantics and also allows harnessing the irregularities of input features. When used for long-term prediction, FREE has the flexibility to incorporate newly collected observations to enhance future prediction. The efficacy of FREE is evaluated in the context of two societally important real-world applications, predicting stream water temperature in the Delaware River Basin and predicting annual corn yield in Illinois and Iowa. Beyond the superior predictive performance over multiple baseline methods, FREE is shown to be more data- and computation-efficient as it can be pre-trained on simulated data generated by physics-based models.

Related papers

• Learning to learn ecosystems from limited data -- a meta-learning approach [0.0]
  We develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems. We show that the framework is capable of accurately reconstructing the dynamical climate'' of the ecological system with limited data.
  arXiv  Detail & Related papers  (2024-10-02T16:23:34Z)
• LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models [25.047123247476016]
  LITE is a large language model for environmental ecosystems modeling. It unifies different environmental variables by transforming them into natural language descriptions and line graph images. During this step, the incomplete features are imputed by a sparse Mixture-of-Experts framework.
  arXiv  Detail & Related papers  (2024-04-01T15:14:07Z)
• LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment [59.320414108383055]
  We present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation. We propose a huge human motion dataset, named FreeMotion, which is collected in various scenarios with diverse human poses.
  arXiv  Detail & Related papers  (2024-02-27T03:08:44Z)
• Foundation Models for Generalist Geospatial Artificial Intelligence [3.7002058945990415]
  This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive data. We have utilized this framework to create Prithvi, a transformer-based foundational model pre-trained on more than 1TB of multispectral satellite imagery.
  arXiv  Detail & Related papers  (2023-10-28T10:19:55Z)
• FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation [13.745581787463962]
  We introduce FaIRGP, a data-driven emulator that satisfies the physical temperature response equations of an energy balance model. We show how FaIRGP can be used to obtain estimates of top-of-atmosphere radiative forcing. We hope that this work will contribute to widening the adoption of data-driven methods in climate emulation.
  arXiv  Detail & Related papers  (2023-07-14T08:43:36Z)
• OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning [67.07363529640784]
  We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather. We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
  arXiv  Detail & Related papers  (2023-06-20T03:02:14Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Causal Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.