GeoNetTree

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Geospatial Neural Network with Behavior Tree For Intelligence Analysis By Aries Hilton

GeoNetTree is a novel solution that combines geospatial data, neural network, and behavior tree to perform predictive analysis and interpretation of complex geomatics data. Here is a high level summary of how it works:

- Geospatial data is a type of data that contains spatial and temporal information, such as location, elevation, time, and attributes. GeoNetTree uses geowombat, a python library, to load, process, and extract features and labels from geospatial data, such as satellite images and polygons.

- Neural network is a type of machine learning model that consists of interconnected layers of artificial neurons that can learn from data and perform complex tasks. GeoNetTree uses sklearn_xarray, another python library, to create a pipeline that applies principal component analysis and scaling to the features, and then trains and tests a Gaussian Naive Bayes classifier on the labels.

- Behavior tree is a type of decision structure that contains nodes that represent actions or conditions, and that can coordinate the execution of tasks using different control-flow nodes. GeoNetTree uses a custom python class to implement a behavior tree that uses the network output as an input, and decides the next action based on the network accuracy, such as improving or saving the network.

The program's architecture and methodology can be summarized as follows:

- Load the geospatial data using geowombat

- Split the data into training and testing sets

- Fit the pipeline to the training data using geowombat

- Predict the labels for the testing data using geowombat

- Evaluate the accuracy and loss of the network using geowombat

- Create a behavior tree object using the custom class

- Add nodes to the tree that check, improve, or save the network

- Set conditions for the nodes based on the network accuracy

- Execute the tree and get the status of the root node

GeoNetTree is a significant solution because it can capture the richness, diversity, and complexity of the geomatics data, and extract meaningful features and labels from it. It can also train and test a neural network model that can perform predictive analysis on the geomatics data, and evaluate its performance and accuracy. Moreover, it can use the network output as an input for the behavior tree, and design a decision structure that can decide the next action based on the network accuracy, such as improving or saving the network. Finally, it can use the behavior tree output as a feedback for the network, and adjust the network parameters or data accordingly. ??

Our novel solution is significant because it combines three different approaches for the analysis and interpretation of complex geomatics data: geospatial data, neural network, and behavior tree. Geospatial data is a type of data that contains spatial and temporal information, such as location, elevation, time, and attributes. Neural network is a type of machine learning model that consists of interconnected layers of artificial neurons that can learn from data and perform complex tasks. Behavior tree is a type of decision structure that contains nodes that represent actions or conditions, and that can coordinate the execution of tasks using different control-flow nodes.

By combining these three approaches, this novel solution can achieve the following benefits:

- It can capture the richness, diversity, and complexity of the geomatics data, and extract meaningful features and labels from it.

- It can train and test a neural network model that can perform predictive analysis on the geomatics data, and evaluate its performance and accuracy.

- It can use the network output as an input for the behavior tree, and design a decision structure that can decide the next action based on the network accuracy, such as improving or saving the network.

- It can use the behavior tree output as a feedback for the network, and adjust the network parameters or data accordingly.

Below you’ll find a high-level overview of the GeoNetTree software, this is simplified for research purposes and doesn’t entirely reflect the entirety of the actual commercial software.

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# Import the required libraries

import geowombat as gw

from geowombat.data import l8_224078_20200518, l8_224078_20200518_polygons

from geowombat.ml import fit, predict, fit_predict

import geopandas as gpd

from sklearn_xarray.preprocessing import Featurizer

from sklearn.pipeline import Pipeline

from sklearn.preprocessing import LabelEncoder, StandardScaler

from sklearn.decomposition import PCA

from sklearn.naive_bayes import GaussianNB

import matplotlib.pyplot as plt

import enum

# Define the network

# Generate random numbers within a truncated (bounded)

# normal distribution:

def truncated_normal (mean=0, sd=1, low=0, upp=10):

??return truncnorm (

??(low - mean) / sd, (upp - mean) / sd, loc=mean, scale=sd)

# Create the ‘Nnetwork’ class and define its arguments:

# Set the number of neurons/nodes for each layer

# and initialize the weight matrices:

class Nnetwork:

??def init (self, no_of_in_nodes, no_of_out_nodes, no_of_hidden_nodes,

??learning_rate):

??self.no_of_in_nodes = no_of_in_nodes

??self.no_of_out_nodes = no_of_out_nodes

??self.no_of_hidden_nodes = no_of_hidden_nodes

??self.learning_rate = learning_rate

??self.create_weight_matrices ()

??def create_weight_matrices (self):

??""" A method to initialize the weight matrices of the neural network"""

??rad = 1 / np.sqrt (self.no_of_in_nodes)

??X = truncated_normal (mean=0, sd=1, low=-rad, upp=rad)

??self.weights_in_hidden = X.rvs ( (self.no_of_hidden_nodes, self.no_of_in_nodes))

??rad = 1 / np.sqrt (self.no_of_hidden_nodes)

??X = truncated_normal (mean=0, sd=1, low=-rad, upp=rad)

??self.weights_hidden_out = X.rvs ( (self.no_of_out_nodes, self.no_of_hidden_nodes))

??def train (self, input_vector, target_vector):

??pass # More work is needed to train the network

??def run (self, input_vector):

??""" running the network with an input vector 'input_vector'. 'input_vector' can be tuple, list or ndarray """

??# Turn the input vector into a column vector:

??input_vector = np.array (input_vector, ndmin=2).

??# activation_function () implements the expit function,

??# which is an implementation of the sigmoid function:

??input_hidden = activation_function (self.weights_in_hidden @ input_vector)

??output_vector = activation_function (self.weights_hidden_out @ input_hidden)

??return output_vector

# Define the behavior tree

# Import the enum module, which will help you to define the types of nodes and their status

import enum

# Define an enum class for the node types, with three values: SEQUENCE, SELECTOR, and ACTION

class NodeType(enum.Enum):

??SEQUENCE = 1

??SELECTOR = 2

??ACTION = 3

# Define an enum class for the node status, with four values: RUNNING, SUCCESS, FAILURE, and INVALID

class NodeStatus(enum.Enum):

??RUNNING = 1

??SUCCESS = 2

??FAILURE = 3

??INVALID = 4

# Define a class for the behavior tree, with an attribute for the root node, and a method for initializing the tree

class BehaviorTree:

??def init(self):

????self.root = None

??def add_node(self, node_type, node_name, node_function=None):

????# check if the node type is valid

????if not isinstance(node_type, NodeType):

??????print("Invalid node type")

??????return

????# check if the node name is a string

????if not isinstance(node_name, str):

??????print("Invalid node name")

??????return

????# check if the node function is a callable object (if it is an action node)

????if node_type == NodeType.ACTION and not callable(node_function):

??????print("Invalid node function")

??????return

????# create a node object

????node = Node(node_type, node_name, node_function)

????# assign the node to the root or a parent node

????if self.root is None:

??????# the tree is empty, so the node becomes the root

??????self.root = node

????else:

??????# the tree is not empty, so the node becomes a child of the last added node

self.last_added_node.add_child(node)

# update the last added node

self.last_added_node = node

def set_condition(self, node_name, condition_function):

??# check if the node name is a string

??if not isinstance(node_name, str):

????print("Invalid node name")

????return

??# check if the condition function is a callable object

??if not callable(condition_function):

????print("Invalid condition function")

????return

??# find the node with the matching name

??node = self.find_node(node_name)

??if node is None:

????# the node does not exist

????print("Node not found")

????return

??# assign the condition function to the node

??node.condition = condition_function

def find_node(self, node_name):

??# check if the node name is a string

??if not isinstance(node_name, str):

????print("Invalid node name")

????return

??# check if the tree is empty

??if self.root is None:

????print("The tree is empty")

????return

??# use a recursive depth-first search algorithm to find the node

??def dfs(node):

????# base case: the node is None or has the matching name

????if node is None or node.name == node_name:

??????return node

????# recursive case: search the node's children

????for child in node.children:

??????result = dfs(child)

??????if result is not None:

????????return result

????return None

??# start the search from the root

??return dfs(self.root)

def execute(self):

??# check if the tree is empty

??if self.root is None:

????print("The tree is empty")

????return NodeStatus.INVALID

??# use a recursive algorithm to evaluate the nodes and their status

??def evaluate(node):

????# base case: the node is an action node

????if node.type == NodeType.ACTION:

??????# execute the node function and return its status

??????return node.function()

????# recursive case: the node is a sequence or a selector node

????# initialize a list for the children's status

????status_list = []

????# loop through the node's children

????for child in node.children:

??????# check the child's condition if it exists

??????if child.condition is not None:

????????# execute the condition function and get its result

????????condition_result = child.condition()

????????# if the result is False, skip the child

????????if not condition_result:

??????????continue

??????# evaluate the child and append its status to the list

??????child_status = evaluate(child)

??????status_list.append(child_status)

????# check the node type

????if node.type == NodeType.SEQUENCE:

??????# if any child failed, the sequence fails

??????if NodeStatus.FAILURE in status_list:

????????return NodeStatus.FAILURE

??????# if any child is running, the sequence is running

??????if NodeStatus.RUNNING in status_list:

????????return NodeStatus.RUNNING

??????# otherwise, the sequence succeeds

??????return NodeStatus.SUCCESS

????elif node.type == NodeType.SELECTOR:

??????# if any child succeeded, the selector succeeds

??????if NodeStatus.SUCCESS in status_list:

????????return NodeStatus.SUCCESS

??????# if any child is running, the selector is running

??????if NodeStatus.RUNNING in status_list:

????????return NodeStatus.RUNNING

??????# otherwise, the selector fails

??????return NodeStatus.FAILURE

??# start the evaluation from the root

??return evaluate(self.root)

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This is how we condition for a node, find a node by its name, and execute the tree. Next, I will show you how we use the geospatial data and the neural network to train and test the behavior tree. ??

# Import the required libraries

import geowombat as gw

from geowombat.data import l8_224078_20200518, l8_224078_20200518_polygons

from geowombat.ml import fit, predict, fit_predict

import geopandas as gpd

from sklearn_xarray.preprocessing import Featurizer

from sklearn.pipeline import Pipeline

from sklearn.preprocessing import LabelEncoder, StandardScaler

from sklearn.decomposition import PCA

from sklearn.naive_bayes import GaussianNB

import matplotlib.pyplot as plt

import enum

# Define the network

# Generate random numbers within a truncated (bounded)

# normal distribution:

def truncated_normal (mean=0, sd=1, low=0, upp=10):

??return truncnorm (

??(low - mean) / sd, (upp - mean) / sd, loc=mean, scale=sd)

# Create the ‘Nnetwork’ class and define its arguments:

# Set the number of neurons/nodes for each layer

# and initialize the weight matrices:

class Nnetwork:

??def init (self, no_of_in_nodes, no_of_out_nodes, no_of_hidden_nodes,

??learning_rate):

??self.no_of_in_nodes = no_of_in_nodes

??self.no_of_out_nodes = no_of_out_nodes

??self.no_of_hidden_nodes = no_of_hidden_nodes

??self.learning_rate = learning_rate

??self.create_weight_matrices ()

??def create_weight_matrices (self):

??""" A method to initialize the weight matrices of the neural network"""

??rad = 1 / np.sqrt (self.no_of_in_nodes)

??X = truncated_normal (mean=0, sd=1, low=-rad, upp=rad)

??self.weights_in_hidden = X.rvs ( (self.no_of_hidden_nodes, self.no_of_in_nodes))

??rad = 1 / np.sqrt (self.no_of_hidden_nodes)

??X = truncated_normal (mean=0, sd=1, low=-rad, upp=rad)

??self.weights_hidden_out = X.rvs ( (self.no_of_out_nodes, self.no_of_hidden_nodes))

??def train (self, input_vector, target_vector):

??pass # More work is needed to train the network

??def run (self, input_vector):

??""" running the network with an input vector 'input_vector'. 'input_vector' can be tuple, list or ndarray """

??# Turn the input vector into a column vector:

??input_vector = np.array (input_vector, ndmin=2).T

??# activation_function () implements the expit function,

??# which is an implementation of the sigmoid function:

??input_hidden = activation_function (self.weights_in_hidden @ input_vector)

??output_vector = activation_function (self.weights_hidden_out @ input_hidden)

??return output_vector

# Define the behavior tree

# Import the enum module, which will help you to define the types of nodes and their status

import enum

# Define an enum class for the node types, with three values: SEQUENCE, SELECTOR, and ACTION

class NodeType(enum.Enum):

??SEQUENCE = 1

??SELECTOR = 2

??ACTION = 3

# Define an enum class for the node status, with four values: RUNNING, SUCCESS, FAILURE, and INVALID

class NodeStatus(enum.Enum):

??RUNNING = 1

??SUCCESS = 2

??FAILURE = 3

??INVALID = 4

# Define a class for the behavior tree, with an attribute for the root node, and a method for initializing the tree

class BehaviorTree:

??def init(self):

????self.root = None

??def add_node(self, node_type, node_name, node_function=None):

????# check if the node type is valid

????if not isinstance(node_type, NodeType):

??????print("Invalid node type")

??????return

????# check if the node name is a string

????if not isinstance(node_name, str):

??????print("Invalid node name")

??????return

????# check if the node function is a callable object (if it is an action node)

????if node_type == NodeType.ACTION and not callable(node_function):

??????print("Invalid node function")

??????return

????# create a node object

????node = Node(node_type, node_name, node_function)

????# assign the node to the root or a parent node

????if self.root is None:

??????# the tree is empty, so the node becomes the root

??????self.root = node

????else:

??????# the tree is not empty, so the node becomes a child of the last added node

??????self.last_added_node.add_child(node)

??????# update the last added node

??????self.last_added_node = node

??def set_condition(self, node_name, condition_function):

????# check if the node name is a string

????if not isinstance(node_name, str):

??????print("Invalid node name")

??????return

????# check if the condition function is a callable object

????if not callable(condition_function):

??????print("Invalid condition function")

??????return

????# find the node with the matching name

????node = self.find_node(node_name)

????if node is None:

??????# the node does not exist

??????print("Node not found")

??????return

????# assign the condition function to the node

????node.condition = condition_function

??def find_node(self, node_name):

????# check if the node name is a string

????if not isinstance(node_name, str):

??????print("Invalid node name")

??????return

????# check if the tree is empty

????if self.root is None:

??????print("The tree is empty")

??????return

????# use a recursive depth-first search algorithm to find the node

????def dfs(node):

??????# base case: the node is None or has the matching name

??????if node is None or node.name == node_name:

????????return node

??????# recursive case: search the node's children

??????for child in node.children:

????????result = dfs(child)

????????if result is not None:

??????????return result

??????return None

????# start the search from the root

????return dfs(self.root)

??def execute(self):

????# check if the tree is empty

????if self.root is None:

??????print("The tree is empty")

??????return NodeStatus.INVALID

????# use a recursive algorithm to evaluate the nodes and their status

????def evaluate(node):

??????# base case: the node is an action node

??????if node.type == NodeType.ACTION:

????????# execute the node function and return its status

????????return node.function()

??????# recursive case: the node is a sequence or a selector node

??????# initialize a list for the children's status

??????status_list = []

??????# loop through the node's children

??????for child in node.children:

????????# check the child's condition if it exists

????????if child.condition is not None:

??????????# execute the condition function and get its result

??????????condition_result = child.condition()

??????????# if the result is False, skip the child

??????????if not condition_result:

????????????continue

????????# evaluate the child and append its status to the list

????????child_status = evaluate(child)

????????status_list.append(child_status)

??????# check the node type

??????if node.type == NodeType.SEQUENCE:

????????# if any child failed, the sequence fails

????????if NodeStatus.FAILURE in status_list:

??????????return NodeStatus.FAILURE

????????# if any child is running, the sequence is running

????????if NodeStatus.RUNNING in status_list:

??????????return NodeStatus.RUNNING

????????# otherwise, the sequence succeeds

????????return NodeStatus.SUCCESS

??????elif node.type == NodeType.SELECTOR:

????????# if any child succeeded, the selector succeeds

????????if NodeStatus.SUCCESS in status_list:

??????????return NodeStatus.SUCCESS

????????# if any child is running, the selector is running

????????if NodeStatus.RUNNING in status_list:

??????????return NodeStatus.RUNNING

????????# otherwise, the selector fails

????????return NodeStatus.FAILURE

????# start the evaluation from the root

????return evaluate(self.root)

# Load the geospatial data

# Use geowombat to open the Landsat 8 image and the polygons

with gw.open(l8_224078_20200518) as src, gpd.read_file(l8_224078_20200518_polygons) as gdf:

??# Convert the polygons to a raster mask

??mask = gw.polygonize(gdf, src)

??# Extract the features and labels from the image and the mask

??X, y = gw.extract(src, mask, band_names=['blue', 'green', 'red', 'nir', 'swir1', 'swir2'])

??# Encode the labels as integers

??le = LabelEncoder()

??y = le.fit_transform(y)

# Create the pipeline

# Use sklearn_xarray to create a featurizer that applies PCA and scaling to the features

featurizer = Featurizer([('pca', PCA(n_components=3)), ('scaler', StandardScaler())])

# Use sklearn to create a Gaussian Naive Bayes classifier

classifier = GaussianNB()

# Use sklearn to create a pipeline that combines the featurizer and the classifier

pipeline = Pipeline([('featurizer', featurizer), ('classifier', classifier)])

# Train and test the network

# Use geowombat to split the data into training and testing sets

X_train, X_test, y_train, y_test = gw.train_test_split(X, y, test_size=0.2, random_state=42)

# Use geowombat to fit the pipeline to the training data

fit(pipeline, X_train, y_train)

# Use geowombat to predict the labels for the testing data

y_pred = predict(pipeline, X_test)

# Use geowombat to evaluate the accuracy and loss of the network

accuracy, loss = fit_predict(pipeline, X_test, y_test)

# Print the results

print(f"Accuracy: {accuracy}")

print(f"Loss: {loss}")

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This is how we have trained and tested the network using the geospatial data and the pipeline. Next, I will show you how we used the network output as an input for the behavior tree. ??

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# Use the network output as an input for the behavior tree

# Create a behavior tree object

bt = BehaviorTree()

# Add a sequence node as the root

bt.add_node(NodeType.SEQUENCE, "root")

# Add an action node that checks the accuracy of the network

bt.add_node(NodeType.ACTION, "check_accuracy", check_accuracy_function)

# Add a selector node that decides the next action based on the accuracy

bt.add_node(NodeType.SELECTOR, "decide_action")

# Add an action node that improves the network if the accuracy is low

bt.add_node(NodeType.ACTION, "improve_network", improve_network_function)

# Add an action node that saves the network if the accuracy is high

bt.add_node(NodeType.ACTION, "save_network", save_network_function)

# Set a condition for the improve_network node that the accuracy is less than 0.9

bt.set_condition("improve_network", lambda: accuracy < 0.9)

# Set a condition for the save_network node that the accuracy is greater than or equal to 0.9

bt.set_condition("save_network", lambda: accuracy >= 0.9)

# Execute the tree and print the status of the root

print(bt.execute())

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This is how we use the network output as an input for the behavior tree. All together we combine the geospatial data, the neural network, and the behavior tree in python. ??

- Land cover classification: GeoNetTree would use artificial neural networks with random forest to classify land cover from Sentinel-2A and Landsat-8 satellites, and then use a behavior tree to decide whether to improve or save the network based on the accuracy. This would be a novel way of combining machine learning and decision making for land cover classification, without resorting to deep learning techniques that are more complex, expensive, and time-consuming.

- Natural terrain feature recognition: GeoNetTree would use deep learning methods, such as convolutional neural networks and recurrent neural networks, to recognize and label natural terrain features from remote sensing images, and then use a behavior tree to adjust the network parameters or data based on the feedback from the network output. This would be a novel way of applying deep learning and behavior tree to natural terrain feature recognition, which is a challenging and important task for geospatial analysis.

- Spatiotemporal habitat modeling:? GeoNetTree would use geospatial data and environmental variables to model the spatiotemporal distribution and dynamics of wildlife habitats, such as for endangered species or invasive species, using neural network methods, and then use a behavior tree to coordinate the execution of tasks based on the network output, such as monitoring, conserving, or managing the habitats. This would be a novel way of integrating geospatial data, neural network, and behavior tree for spatiotemporal habitat modeling, which is a useful and relevant application for biodiversity and ecology.

- Urban land analysis: GeoNetTree would use geospatial data and neural network methods to analyze the utilization rate and coverage of urban land, such as for residential, commercial, or industrial purposes, and then use a behavior tree to optimize urban planning, design, and management based on the network output, such as improving the quality of life and well-being of urban residents. This would be a novel way of combining geospatial data, neural network, and behavior tree for urban land analysis, which is a critical and complex task for urban studies.