STORM/FACT REASONER Workflow Logs

[NISH1001]

Describe the bug

There are some weird bugs in the output of the storm. This thread is to track that.

Steps to reproduce the behavior

No response

Expected Behavior

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Relevant log output

[NISH1001]

Incorrectly Formatted Markdown for citation

cc: @movinam

[NISH1001]

Re: Demo from May 13,

The output also sometimes gives backtick "`" as codeblock

---

Sample Output (after removing backtick):

'markdown\n# Literature Review on Oil Palm Classification Using Remote Sensing and Machine Learning (2020-2025)\n\n## Introduction\n\nOil palm classification is pivotal in modern agriculture, primarily due to its significant economic and environmental impacts. The ability to accurately classify oil palm plantations supports effective land management, crop monitoring, and sustainable agricultural practices. Recognizing the vast plantations across tropical regions, such as Southeast Asia and Africa, oil palm classification is instrumental in addressing issues related to deforestation, biodiversity loss, and carbon emissions.\n\nRemote sensing has emerged as a crucial technology in this field, offering comprehensive spatial and temporal data that enhances the ability to monitor and manage large-scale agricultural activities. It allows researchers and practitioners to gather valuable insights into plantation health, spatial distribution, and ecosystem changes without extensive fieldwork. The development of high-resolution satellite imagery, synthetic aperture radar, and aerial data collection has greatly improved the precision with which these plantations can be analyzed.\n\nSimultaneously, machine learning has transformed how remote sensing data is utilized, enabling the automation and accuracy of classification tasks. Machine learning algorithms, through their ability to process vast datasets and identify complex patterns, aid in extracting meaningful information from the rich data captured via remote sensing technologies. By integrating these cutting-edge technologies, new methodologies have evolved, enhancing the precision of oil palm classification and supporting environmental conservation efforts.\n\n## Advancements in Remote Sensing for Oil Palm Classification (2020-2025)\n\nBetween 2020 and 2025, significant advancements in remote sensing technologies have substantially improved the classification of oil palm plantations. The integration of high-resolution satellite imagery, advanced Synthetic Aperture Radar (SAR) technologies, and aerial data collection has enhanced the accuracy and efficiency of mapping and monitoring oil palm cultivation.\n\n### Satellite Imagery Developments\n\nThe period from 2020 to 2025 has seen leaps in the resolution and availability of satellite imagery, thanks to developments by companies like Planet Labs and Maxar Technologies. These high-resolution images have played a critical role in enhancing the accuracy of oil palm classification models. High-frequency imaging capabilities allow for more precise temporal monitoring of plant growth and stress conditions. Additionally, the increased spectral band options provide more detailed data for distinguishing oil palms from other vegetation types[1].\n\n### Emergence of SAR Technologies\n\nSynthetic Aperture Radar (SAR) technologies, notably from platforms such as Sentinel-1, have emerged as vital tools for consistent remote sensing in oil palm classification. SAR\'s ability to penetrate cloud cover and collect data under various weather conditions is particularly advantageous in tropical regions where oil palms typically grow. This capability ensures uninterrupted monitoring, which is crucial for dynamic environments and during different seasons[2].\n\n### Aerial Data Collection Advances\n\nDrone technologies have substantially contributed to the detailed analysis of oil palm plantations. The utilization of drones equipped with multispectral and hyperspectral sensors allows for the collection of high-resolution data, which supports precise machine learning applications. These sensors can identify subtle variations in plant conditions, enhancing the precision of classification models[3].\n\n## Machine Learning Models for Oil Palm Classification\n\nMachine learning models have played a pivotal role in advancing the classification of oil palm plantations through the integration of remote sensing data. With the rapid evolution of remote sensing technologies, these models have enabled more accurate, efficient, and scalable classification processes.\n\n### Convolutional Neural Networks (CNNs)\n\nConvolutional Neural Networks (CNNs) have emerged as one of the most effective machine learning techniques for automating feature extraction from remote sensing imagery. Their ability to capture spatial hierarchies of features makes them exceptionally suited for oil palm classification. CNNs have demonstrated significant improvements in classification accuracy by effectively learning complex patterns directly from the data, thereby reducing the need for manual feature selection and engineering[4].\n\n### Random Forests and Support Vector Machines\n\nRandom Forests (RF) and Support Vector Machines (SVMs) are traditional machine learning models that continue to be popular in oil palm classification due to their robustness and versatility. RF is particularly well-suited for handling high-dimensionality datasets, making it a reliable choice for processing complex remote sensing imagery. It operates by constructing a multitude of decision trees at training time and outputting the mode of the classes for classification tasks[5]. SVMs, on the other hand, are known for their ability to create a hyperplane that best segregates different classes in the feature space[6].\n\n### Ensemble and Hybrid Models\n\nIn recent years, the application of ensemble and hybrid models in oil palm classification has seen considerable growth. These models are designed to improve classification accuracies by combining multiple machine learning algorithms to leverage their individual strengths. Techniques such as stacking and boosting have been particularly successful. Stacking involves training a learning algorithm to combine the predictions of several other models, each having been trained independently[7].\n\n## Identified Research Gaps\n\nThe field of oil palm classification using remote sensing and machine learning has seen significant growth, yet several research gaps remain prominent. These gaps hinder the full potential application of these technologies in agricultural monitoring and sustainable practices.\n\n### Temporal Dynamics in Data\n\nOne of the foremost gaps identified in current methodologies is the insufficient use of temporal data for oil palm ecosystems. Temporal dynamics are crucial for monitoring changes in plantation growth, detecting disease outbreaks, and managing harvest cycles[8].\n\n### Generalization Across Ecosystems\n\nAnother challenge is the generalization of classification models across diverse environmental conditions. Oil palm plantations are spread across varied geographical regions with distinct climatic and ecological characteristics. Models developed for specific regions often underperform when applied to different areas[9].\n\n### Data Imbalance and Quality\n\nData quality and imbalance in training datasets present significant hurdles in the effective classification of oil palm plantations. Datasets often have a disproportionate number of positive and negative samples, which can skew the results of machine learning models, leading to poor classification performance[10].\n\n## Annotated Datasets for Oil Palm Classification\n\nThe availability of annotated datasets is crucial for the effective development and validation of machine learning models in oil palm classification.\n\n### Publicly Available Datasets\n\nPublicly available datasets have been instrumental in democratizing research in oil palm classification. Satellite imagery datasets such as Sentinel-2 and Landsat have been frequently used due to their accessibility and detailed spectral information[11].\n\n### Proprietary Datasets and Benchmarks\n\nProprietary datasets, while not as accessible, offer high-quality annotated data custom-tailored to specific projects, allowing for superior accuracy in classification tasks[12].\n\n## Environmental Implications of Oil Palm Cultivation\n\nThe cultivation of oil palm carries significant environmental implications, necessitating thorough examination to ensure sustainable practices.\n\n### Deforestation and Biodiversity Loss\n\nOil palm cultivation has been a major driver of deforestation in tropical regions, particularly in Southeast Asia. As forests are cleared to make way for plantations, there is an immediate loss of habitat for innumerable species, leading to significant reductions in biodiversity[13].\n\n### Greenhouse Gas Emissions and Soil Degradation\n\nThe process of converting forests and peatlands into oil palm plantations releases a significant amount of stored carbon into the atmosphere, contributing to greenhouse gas emissions. Peatlands, in particular, are carbon-rich ecosystems, and their drainage for cultivation leads to substantial greenhouse gases being emitted[14].\n\n### Water Quality and Ecosystem Balance\n\nThe establishment and operation of oil palm plantations can significantly affect local water cycles and quality. Plantation activities often alter the natural flow of surface waters, leading to issues such as increased runoff and sedimentation in rivers and streams[15].\n\n## Future Research Avenues\n\nThe field of oil palm classification through remote sensing and machine learning has seen substantial advancements over recent years. Despite the progress, there are still critical gaps and opportunities for further research that could significantly enhance classification methodologies and address various environmental concerns.\n\n### Innovative Model Enhancements\n\nOne of the vital areas for future research is the development of innovative model enhancements, particularly focusing on temporal analysis and the generalization of models across diverse ecosystems[16].\n\n### Data Collection and Integration\n\nAdvanced data collection techniques and the integration of multiple remote sensing technologies are critical areas for research enhancement. The combination of high-resolution satellite imagery, synthetic aperture radar, and aerial drone technologies could offer comprehensive data inputs, improving the robustness and accuracy of classification models[17].\n\n### Interdisciplinary Approaches in Research\n\nIncorporating interdisciplinary approaches that combine insights from fields such as ecology, economics, and social sciences is another promising direction for future research. Understanding the broader impacts of oil palm cultivation requires a holistic view that considers environmental sustainability, economic viability, and social implications[18].\n\n## References\n\n1. Planet Labs and Maxar Technologies developments in satellite imagery. [Planet Labs](https://www.planet.com/) [Maxar Technologies]([https://www.maxar.com)\n2](https://www.maxar.com)/n2). Sentinel-1 SAR data applications. [ESA Sentinel-1]([https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1)\n3](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1)/n3). Drone technology with multispectral sensors. [Drone Information Source]([https://www.droneinfo.com)\n4](https://www.droneinfo.com)/n4). CNNs for feature extraction in remote sensing. [Machine Learning Blog]([https://www.ml-blog.com)\n5](https://www.ml-blog.com)/n5). Random Forests in remote sensing applications. [Random Forest Source]([https://www.randomforestsource.com)\n6](https://www.randomforestsource.com)/n6). Support Vector Machines (SVMs). [SVM Information]([https://www.svminfo.com)\n7](https://www.svminfo.com)/n7). Ensemble learning approaches. [Ensemble Techniques]([https://www.ensembletechniques.com)\n8](https://www.ensembletechniques.com)/n8). Johnson, R. et al. (2023). "Temporal Dynamics in Oil Palm Classification using Remote Sensing and Machine Learning." *Journal of Agricultural Research*, 45(3), 234-247.\n9. Generalization challenges in remote sensing. [Generalization Source]([https://www.generalizationsource.com)\n10](https://www.generalizationsource.com)/n10). Data quality and imbalance challenges. [Data Management Resource]([https://www.datamanagementresource.com)\n11](https://www.datamanagementresource.com)/n11). Sentinel-2 and Landsat data. [USGS Landsat](https://www.usgs.gov/landsat) [ESA Sentinel-2]([https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)\n12](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)/n12). Proprietary datasets for remote sensing. [Proprietary Data]([https://www.proprietarydata.com)\n13](https://www.proprietarydata.com)/n13). Deforestation and biodiversity impacts. [Deforestation Impact]([https://www.deforestationimpact.com)\n14](https://www.deforestationimpact.com)/n14). Greenhouse gas emissions from peatlands. [Peatlands Emissions]([https://www.peatlandemissions.com)\n15](https://www.peatlandemissions.com)/n15). Water quality issues in plantations. [Water Quality Source]([https://www.waterqualitysource.com)\n16](https://www.waterqualitysource.com)/n16). Williams, S. & Zhang, L. (2022). "Integrating SAR and Optical Satellite Data for Enhanced Classification of Oil Palm Regions." *Remote Sensing Technology*, 12(6), 678-689.\n17. Martinez, A. & Green, J. (2023). "The Socio-Economic Impacts of Oil Palm Plantation Expansion: A Multi-Disciplinary Approach." *Environment and Development Studies*, 18(2), 113-129.\n\n'

[movinam]

TODOs:

• Need a separate citing mechanism independent of the model being used.
• Integrate the existing search tool, retriever and vector DB.
• Make the nodes independent of storm (for example, the interview process).

[NISH1001]

Running on full output of storm:

Atom a1: There is increasing global demand for palm oil.
Atom a2: Effective techniques for monitoring and managing oil palm plantations have become imperative.
Atom a3: Remote sensing technologies have emerged as powerful tools in oil palm classification.
Atom a4: Advancements in machine learning have enhanced oil palm classification.
Atom a5: Remote sensing technologies offer precision and scale that traditional methods cannot match.
Atom a6: Remote sensing technologies facilitate large-scale analysis by enabling the automated classification of oil palm crops.
Atom a7: Automated classification of oil palm crops is crucial for yield estimation.
Atom a8: Automated classification of oil palm crops is crucial for plantation expansion monitoring.
Atom a9: Automated classification of oil palm crops is crucial for ecological impact assessment.
Atom a10: From 2020 to 2025, there have been significant strides in the integration of remote sensing and machine learning for oil palm classification.
Atom a11: The literature review provides a comprehensive synthesis of recent advancements in oil palm classification.
Atom a12: The literature review explores innovative methodologies, datasets, and trends in oil palm classification.
Atom a13: The literature review highlights the evolving landscape of remote sensing technologies.
Atom a14: The literature review highlights the increasingly sophisticated application of machine learning models.
Atom a15: The review aims to offer insights into current capabilities and limitations in oil palm classification.
Atom a16: The review identifies potential pathways for future research in oil palm classification.
Atom a17: Remote sensing techniques have become a cornerstone in oil palm classification.
Atom a18: Satellite imagery provides high temporal and spatial resolution data for monitoring changes.
Atom a19: LiDAR offers precision in three-dimensional mapping for estimating canopy structures and biomass.
Atom a20: Hyperspectral imaging captures a broad spectrum of light to distinguish oil palm trees from other vegetation.
Atom a21: Drones enable close-range sensing that captures high-quality images rapidly and at lower costs.
Atom a22: Random Forest models have been widely used in oil palm classification.
Atom a23: Support Vector Machines provide effective linear and non-linear data classification.
Atom a24: Convolutional Neural Networks have emerged as a powerful tool in oil palm classification.
Atom a25: Data preprocessing involves cleaning, normalizing, and transforming data for analysis.
Atom a26: Techniques such as Principal Component Analysis and Recursive Feature Elimination enhance model performance.
Atom a27: Publicly available datasets have become pivotal in advancing research on oil palm classification.
Atom a28: The 'Oil Palm Plantation Dataset' contains satellite imagery from various sources.
Atom a29: Platforms like the Global Land Cover Facility and Google Earth Engine offer vast datasets for studies.
Atom a30: Acquiring high-quality and comprehensive data remains a significant challenge.
Atom a31: Many datasets have limitations in geographic coverage.
Atom a32: Many datasets often focus on specific regions like Southeast Asia.
Atom a33: Privacy and proprietary restrictions inhibit access to certain high-resolution datasets.
Atom a34: Notable advancements in oil palm classification have been driven by enhancements in remote sensing technologies.
Atom a35: High-resolution satellite imagery allows for more precise identification of oil palm areas.
Atom a36: Drones with multispectral sensors have become integral in collecting high-frequency data.
Atom a37: The adoption of deep learning techniques has revolutionized image data processing.
Atom a38: Random Forest models are robust against overfitting.
Atom a39: Support Vector Machines offer strong performance with limited training data.
Atom a40: Convolutional Neural Networks excel in learning spatial hierarchies from image data.
Atom a41: Technological gaps revolve around the resolution and sensitivity of remote sensing equipment.
Atom a42: Current satellite imagery might not always capture fine details necessary for distinguishing land covers.
Atom a43: The potential of hyperspectral and LiDAR sensors is often underutilized due to high costs.
Atom a44: A notable lack of studies integrate socio-economic factors with remote sensing data.
Atom a45: Insufficient training data limits Convolutional Neural Network application.
Atom a46: Research often focuses on single-source data analysis.
Atom a47: Innovative approaches like Recurrent Neural Networks and Long Short-Term Memory networks offer potential for handling temporal data.
Atom a48: Multi-modal data integration from various sensors offers a comprehensive view of oil palm environments.
Atom a49: Future research should focus on data fusion and algorithm development.
Atom a50: Interdisciplinary collaboration is crucial for advancing classification models.
Atom a51: Engaging with experts across various fields can refine models to include ecological and socio-economic factors.
Atom a52: Research from 2020 to 2025 has significantly advanced agritech and environmental monitoring in oil palm classification.
Atom a53: Integration of remote sensing technologies and machine learning algorithms has enhanced accuracy and efficiency in classifying oil palm plantations.
Atom a54: Advancements enable better monitoring and management of oil palm plantations.
Atom a55: Future research should focus on novel machine learning approaches, multi-source data integration, and interdisciplinary collaboration.
[Atoms built: 56]
[FactReasoner] Revise the atoms ...
[AtomReviser] Prompts created: 56