Application of ChatGPT in soil science research and the perceptions of soil scientists in Indonesia

Since its arrival in late November 2022,ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned,conducted,and published using a generative artificial intelligence approach.ChatGPT-4 was released four months later and became more popular in November 2023.However,there is little study about the perception of scientists of these chatbots,especially in soil science.This article presents the new findings of a brief research investigating soil scientists’responses and perceptions towards chatbots in Indonesia.This artificial intelligence application facilitates conversation-based interactions in text format.The study evaluated ten ChatGPT answers to fundamental questions in soil science,which has developed into a normal science with a mutually agreed-upon paradigm.The evaluation was carried out by seven soil scientists recognized for their expertise in Indonesia,using a scale of 1-100.In addition,a questionnaire was distributed to soil scientists at the National Research and Innovation Agency of the Republic of Indonesia(BRIN),universities,and Indonesian Soil Science Society(HITI)members to gauge their perception of ChatGPT’s presence in the research field.The study results indicate that the scores of ChatGPT answers range from 82.99 to 92.24.ChatGPT-4 is better than both the paid and free versions of ChatGPT-3.5.There is no significant difference between the English and Indonesian versions of ChatGPT-4.0.However,the perception of general soil scientists about the level of trust is only 55%.Furthermore,80%of soil scientists believe that chatbots can only be used as digital tools to assist in soil science research and cannot be used without the involvement of soil scientists.

Deep reinforcement learning and 3D physical environments applied to crowd evacuation in congested scenarios

To avoid crowd evacuation simulations depending on 2D environments and real data,we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning(DRL)and 3D physical environments(3DPEs).In 3DPEs,we construct simulation scenarios from the aspects of geometry,semantics and physics,which include the environment,the agents and their interactions,and provide training samples for DRL.In DRL,we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy.With a unified configuration,we conduct evacuation simulations.In scenarios with one exit,we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics(number,mass and height)on evacuation.In scenarios with two exits and a uniform(nonuniform)distribution of agents,we explore the impact of exit characteristics(width and relative position)and agent characteristics(height,initial location and distribution)on agent exit selection and evacuation.Overall,interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation.

Active High-Locality Landslides in Mao County: Early Identification and Deformational Rules

High-locality landslides are located on slopes at high elevations and are characterized by long sliding distances, large gravitational potential energy, high movement velocities, tremendous kinetic energy, and sudden onset. Thus, they often cause catastrophic damage to human lives and engineering facilities. It is of great significance to identify active high-locality landslides in their early deformational stages and to reveal their deformational rules for effective disaster mitigation. Due to alpinecanyon landforms, Mao County is a representative source of high-locality landslides. This work employs multisource data(geological, terrain, meteorological, ground sensor, and remote sensing data) and timeseries In SAR technology to recognize active high-locality landslides in Mao County and to reveal their laws of development. Some new viewpoints are suggested.(1) Nineteen active high-locality landslides are identified by the time-series In SAR technique, of which 7 are newly discovered in this work. All these high-locality landslides possessed good concealment during their early deformational stages. The newly discovered HL-16 landslide featured a large scale and a great slope height, posing a large threat to the surrounding buildings and residents.(2) The high-locality landslides in Mao County were mainly triggered by three factors: earthquakes, precipitation, and road construction.(3) Three typical high-locality landslides that were triggered by different factors are highlighted with their deformational rules under the functions of steep terrain, shattered rocks, fissure-water penetration, precipitation, and road construction. This work may provide clues to the prevention and control of high-locality landslides and can be applied to the determination of active high-locality landslides in other hard-hit areas.

A comprehensive and up-to-date web-based interactive 3D emergency response and visualization system using Cesium Digital Earth: taking landslide disaster as an example

As with the fast advances in the technologies of big Earth data and information communication,Web-based 3D GIS system has come a long way from a few years ago.These advances reflect in many aspects of 3D GIS such as higher real-time performance,enhanced interactivity,more realistic 3D visualization effect and improved user interface.This paper aims to present a comprehensive and upto-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium APl,taking landslide disaster as an example.Building upon recent advances in WebGL technology,we developed a suite of enhanced 3D spatial analysis functions,including interactive route planning,instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications,and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet.Moreover,professional functions such as landslide susceptibility mapping,landslide monitoring,spatial temporal contingency plan management,landslide information management,personnel and equipment management,and communication are all implemented and integrated in the 3D GIS system.Most of the functions of the system are implemented using open-source projects,which is beneficial to the development of the 3D GIS research community.

An efficient flood dynamic visualization approach based on 3D printing and augmented reality

Flood visualization is an effective and intuitive tool for representing flood information from abstract spatiotemporal data.With the growing demand for flood disaster visualizations and mitigation,augmented flood visualizations that support decision makers’perspectives are needed,which can be enhanced by emerging augmented reality(AR)and 3D printing technologies.This paper proposes an innovative flood AR visualization method based on a 3D-printed terrain model and investigates essential techniques,such as the suitable size calculation of the terrain models,the adaptive processing of flood data,and hybridizing virtual flood and terrain models.A prototype experimental system(PES)based on the proposed method and a comparison experimental system(CES)based on a virtual terrain are developed to conduct comparative experiments,which combine the system performance and questionnaire method to evaluate the efficiency and usability of the proposed method.The statistical results indicate that the method is useful for assisting participants in understanding the flood hazard and providing a more intuitive and realistic visual experience compared with that of the traditional AR flood visualization method.The frame rate is stable at 60 frames per second(FPS),which means that the proposed method is more efficient than the traditional AR flood visualization method.

A monocular visual SLAM system augmented by lightweight deep local feature extractor using in-house and low-cost LIDAR-camera integrated device

Simultaneous Localization and Mapping(SLAM)has been widely used in emergency response,self-driving and city-scale 3D mapping and navigation.Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges(e.g.extreme lighting)while the traditional extractors are struggling.In this paper,we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework.In this thread,our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device.The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively.The scale estimation of the monocular visual SLAM is also significantly improved(0.01 versus 0.98).In addition,a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping.This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors.