Mallin dokumentaation varmistaminen jatkuvien koneoppimisjärjestelmien kehityksessä

Context: Over the past years, the development of machine learning (ML) enabled software has seen a rise in popularity. Alongside this trend, new challenges have been identified, such as growing concerns about the use, including the ethical concerns, of ML models, as misuse can lead to severe consequences for human beings. To alleviate this problem, more comprehensive model documentation has been suggested, but how can that documentation be made part of a modern, continuous development process? Objective: We design and develop a solution, which consists of a software artefact and its surrounding process, which enables and moderates continuous documentation of ML models. The solution needs to comply with the modern way-of-working of software development. Method: We apply the design science research methodology to divide the design and development into six separate tasks, i.e., problem identification, objective definition, design and development, demonstration, evaluation, and communication. Results: The solution uses model cards for storing model details. These model cards are tested automatically and manually, forming a quality gate and ensuring integrity of the documentation. The software artefact is implemented in the form of a GitHub Action. Conclusion: We conclude that the software artefact supports and assures proper model documentation in the form of a model card. The artefact allows for customization by the user, thereby supporting domain-specific use cases

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Yksityiskohtaisen metsävaratiedon tuottaminen – kohti täsmämetsätaloutta?

Tieteen tori: Yksityiskohtainen metsävaratiet

The Hidden Cairns : A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed

Effect of airborne laser scanning accuracy on forest stock and yield estimates

The main objective of the study was to assess the magnitude of uncertainty of airborne laser scanning (ALS) -based forest inventory data in forest net present value (NPV) computations. A starting point was the current state of change in operative forest-planning in which traditional standwise field inventories (SWFI) are being replaced by area-based ALS inventories (A_ALS). The more detailed objectives were as follows: 1) to investigate the significance of the accuracy of current (SWFI, A_ALS) and future (ALS individual tree detection (ITD)) forest inventory methodologies applied in the timing of simulated loggings and in NPV computations, 2) to compare the forest-planning inventory methods currently applied with respect to the accuracy of the timber assortment information derived, 3) to investigate the sources of uncertainty related to the estimation of timber assortment volumes and economic values in forest management-planning simulations and 4) to compare the uncertainty related to inventory accuracy, growth models and timber price development in NPV computations at the stand- and forest property-level, using various interest rates. The study was carried out, using empirical and simulated forest inventory data, forest management-planning calculations and Monte Carlo simulations. It was shown that forest inventory errors led to significant mistiming of simulated loggings and subsequent prominent losses in simulated NPV. The most significant source of error in the prediction of timber assortment outturns was SWFI and A_ALS inventory error. The errors related to stem distribution generation, stem form prediction and bucking simulation were significant but considerably lower in magnitude than the inventory error. A_ALS interpretation led to accuracy levels similar to or better than that of SWFI. At the stand-level the growth models used in forest-planning simulation computations were the greatest source of uncertainty with respect to NPVs computed throughout the rotation period. Uncertainty almost as great was caused by A_ALS and SWFI data uncertainty, while the uncertainty caused by fluctuation in timber prices was considerably lower in magnitude. Forest property level deals with a considerably lesser degree of NPV deviation than does stand-level: A_ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation in property-level NPV when an interest rate of 3% was applied. A_ALS inventory error-related uncertainty resulted in significant bias in property-level NPV estimates. The study forms a basis for developing practical methodologies for taking uncertainty into account in forest property valuation

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed.Peer reviewe

Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes

Peer reviewe

Heikkotuottoisten ojitettujen soiden puustoinventointi Maanmittauslaitoksen laserkeilausaineistoa hyödyntäen

TutkimusselosteSeloste artikkelista: Niemi, M., Vastaranta, M., Peuhkurinen, J. & Holopainen, M. 2015. Forest inventory attribute prediction using airborne laser scanning in low-productive forestry-drained boreal peatlands. Silva Fennica 49(2), article id 121

Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

Special Issue on the 39th Canadian Symposium on Remote Sensing (CSRS 2018)Land use and land cover maps are vital sources of information for many applications. Recently, using high-resolution and open-access satellite images has become a preferred method for mapping land cover, especially over large areas. This study was designed to map the land cover and agricultural fields of a large area using Sentinel-1A synthetic aperture radar (SAR) images. Seven machine-learning methods were employed for image analyses. The Random Forest classifier algorithm outperformed the other machine-learning methods in the training step; thus, we selected it for further use and tuned its parameters. After several image processing steps, we classified the final image into 23 land cover classes and achieved an overall accuracy of 42% for all classes, and 57% for agricultural fields. This research note highlights some characteristics and advantages of using Sentinel-1A images and provides novel methods for nation-wide large-area mapping applications.Peer reviewe