Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Old-growth forestAn old-growth forest, sometimes synonymous with primary forest, virgin forest, late seral forest, primeval forest, first-growth forest, or mature forestis a forest that has attained great age without significant disturbance, and thereby exhibits unique ecological features, and might be classified as a climax community. The Food and Agriculture Organization of the United Nations defines primary forests as naturally regenerated forests of native tree species where there are no clearly visible indications of human activity and the ecological processes are not significantly disturbed.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Forest dynamicsForest dynamics describes the underlying physical and biological forces that shape and change a forest ecosystem. The continuous state of change in forests can be summarized with two basic elements: disturbance and succession. Disturbance (ecology) Forest disturbances are events that cause change in the structure and composition of a forest ecosystem, beyond the growth and death of individual organisms.
Regression analysisIn statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.
Intact forest landscapeAn intact forest landscape (IFL) is an unbroken natural landscape of a forest ecosystem and its habitat–plant community components, in an extant forest zone. An IFL is a natural environment with no signs of significant human activity or habitat fragmentation, and of sufficient size to contain, support, and maintain the complex of indigenous biodiversity of viable populations of a wide range of genera and species, and their ecological effects. IFLs are estimated to cover 23 percent of forest ecosystems (13.
Logistic regressionIn statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).
Transformer (machine learning model)A transformer is a deep learning architecture that relies on the parallel multi-head attention mechanism. The modern transformer was proposed in the 2017 paper titled 'Attention Is All You Need' by Ashish Vaswani et al., Google Brain team. It is notable for requiring less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl, by virtue of the parallelized processing of input sequence.
Boreal forest of CanadaCanada's boreal forest is a vast region comprising about one third of the circumpolar boreal forest that rings the Northern Hemisphere, mostly north of the 50th parallel. Other countries with boreal forest include Russia, which contains the majority; the United States in its northernmost state of Alaska; and the Scandinavian or Northern European countries (e.g. Sweden, Finland, Norway and small regions of Scotland). In Europe, the entire boreal forest is referred to as taiga, not just the northern fringe where it thins out near the tree line.
Nonlinear regressionIn statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.
Landsat programThe Landsat program is the longest-running enterprise for acquisition of of Earth. It is a joint NASA / USGS program. On 23 July 1972, the Earth Resources Technology Satellite was launched. This was eventually renamed to Landsat 1 in 1975. The most recent, Landsat 9, was launched on 27 September 2021. The instruments on the Landsat satellites have acquired millions of images.
Diffusion modelIn machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process.
Tropical forestTropical forests (a.k.a. jungle) are forested landscapes in tropical regions: i.e. land areas approximately bounded by the tropic of Cancer and Capricorn, but possibly affected by other factors such as prevailing winds. Some tropical forest types are difficult to categorize. While forests in temperate areas are readily categorized on the basis of tree canopy density, such schemes do not work well in tropical forests. There is no single scheme that defines what a forest is, in tropical regions or elsewhere.
Reinforcement learningReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
Multinomial logistic regressionIn statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).
Secondary forestA secondary forest (or second-growth forest) is a forest or woodland area which has regenerated through largely natural processes after human-caused disturbances, such as timber harvest or agriculture clearing, or equivalently disruptive natural phenomena. It is distinguished from an old-growth forest (primary or primeval forest), which has not recently undergone such disruption, and complex early seral forest, as well as third-growth forests that result from harvest in second growth forests.
