Trend-Based Prediction of Spatial Change
Authors: Xiaoyu Ge, Jae Hee Lee, Jochen Renz, Peng Zhang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method using real world data from two completely different domains, which demonstrates the accuracy of our predictions. We applied the method to two different spatial domains, namely wild fire progression and also cloud movement, where it is more likely that sudden changes or topological changes of a region occur. We use a real-world data set for each domain and the average precision and recall under different settings are summarized in Table. 2. |
| Researcher Affiliation | Academia | Research School of Computer Science, Australian National University {xiaoyu.ge,jochen.renz,p.zhang}@anu.edu.au QCIS, FEIT, University of Technology Sydney jaehee.lee@uts.edu.au |
| Pseudocode | Yes | Algorithm 1: Boundary point prediction |
| Open Source Code | No | The paper does not provide a link to open-source code or state that the code is publicly available. |
| Open Datasets | Yes | Wild Fire Progression: From the data base of USDA forest service2, we obtained a sequence of progression maps of a real wild fire. 2http://www.fs.fed.us/nwacfire/ball/. Cloud Movement We also obtained video data of moving clouds that is recorded using a fisheye lens (see [Wood-Bradley et al., 2012] for detail). |
| Dataset Splits | No | The paper describes using past snapshots for prediction and evaluates on real-world and generated data, but it does not specify explicit training/validation/test splits (e.g., percentages or sample counts) or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers, such as libraries or solvers. |
| Experiment Setup | Yes | For each evaluation, our method takes a sequence RT w+1, . . . , RT of w recent snapshots to predict the outer approximation of regions at T + 1. For our analysis we varied both w and the threshold d as defined in Section 4.3. |