New
Samples
- Automate Road Surface Investigation Using Deep Learning
- Detecting and Categorizing Brick Kilns from Satellite Imagery
- Extracting Slums from Satellite Imagery
- Extracting Sinkholes from Aerial Imagery
- Detecting Settlements Using Supervised Classification and Deep Learning
- How much green is Delhi as on 15 Oct 2017?
- Maximizing Fire Protection Coverage
- Calculate Impervious Surfaces from Multispectral Imagery using Deep Learning
- Safe Streets to Schools
- Spatial and temporal distribution of service calls using big data tools
- Time Series Prediction of AirBnB Properties in New York City
Module Changes
arcgis.raster.functions.gbl
New functions
arcgis.env
- new parameter
type_init_tail_parameters
(Seearcgis.learn
below.)
arcgis.learn
- Adds support for multiple GPU machines to
learn
module - Adds the
DeepLabV3
model based ontorchvision
- includes multispectral data support
- Enhancements to Multispectral data support:
- turn on
DRA
with thestatistics_type
parameter in variousshow_results()
andshow_batch()
functions - adds environment variable
type_init_tail_parameters
to controlModel Tail
initialization forarcgis.learn
functions - Sanctions
classify_objects()
overFeatureClassifier.categorize_features()
for feature categorization - Adds support for training models on either cpu or gpu devices
- Adds support for evaluating MaskRCNN model performance with correct metrics for the trained model to compare the results
- Adds
accuracy()
function toUnetClassifier
- Adds
unet_aux_loss
parameter to thePSPNetClassifier
- Adds support for training a subset of classes from
prepare_data()
class_mapping
parameter toMaskRCNN
- Adds multispectral data support to:
FeatureClassifier
SingleShotDetector
RetinaNet
PSPNetClassifier
MaskRCNN
- Adds support for
resnet18
andresnet34
backbones toMaskRCNN
- Adds support for
batchnorm
unfreezing in *PSPNetClassifier
- Adds support for panchromatic data
- Adds validation to
class_mapping
parameter forprepare_data()
function
Fixes
Module Changes
arcgis.gis.admin
- Fixes formatting in
EnterpriseUsers.update()
function
arcgis.mapping
- Fixes error when drawing
map widget
using Microsoft Edge
arcgis.raster.functios.gbl
- General improvements to documentation
- Improves alphabetical ordering of API Reference
arcgis.learn
- Improves error messaging when calling
from_model()
if proper libraries are not installed - Deprecates the
FeatureClassifier.categorize_features()
method in favor ofclassify_objects()
- Improves messaging when
input_video_path
parameter video does not exist forRetinaNet.predict_video()
andSingleShotDetector.predict_video()
functions - Fixes bug when re-running a previously saved
SingleShotDetector
model - Fixes various model inferencing errors when using
Image Server
- Fixes errors when using
detect_objects()
when raster function configuration information is inaccessible - Fixes error when running
detect_objects()
when using nomodel_arguments
- Fixes
EntityRecognizer.extract_entities()
returning a data frame with an empty column name - Improves messaging when incorrect path passed as
path
argument toprepare_data()
- Fixes error when list of tensors is empty when running
SingleShotDetector.fit()
model - Fixes model accuracy function in
Unet
andPSPNet
to return maximum accuracy if checkpoint isTrue
- Improves tagging scheme documentation for
prepare_data()
function - Improves visual accuracy when using Multispectral imagery with
UNetClassifier
- Fixes missing
supported_backbone
documentation for all models - Improves
predict()
andfrom_model()
documentation on theFeatureClassifier
- Fixes [
show_batch()
] errors on data objects whenclass_mapping
parameter values fromprepare_data()
are non-contiguous - Fixes errors with
show_results()
andfit()
methods ofEntityRecognizer
- Fixes error when
prepare_data()
dataset_type
argument value isBILUO
- Fixes
extract_entities()
error when readingUTF-16
encoded files - Fixes to
load()
function for input paths - Fixes error reading file names in
EntityRecognizer.load()
- Fixes error causing
accuracy()
to always return 1 with certainclass_mapping
values when usingUnetClassifier
- Fixes error where color map values were truncated using
MaskRCNN
- Fixes error in
SingleShotDetector.save()
method by adding optionaloverwrite
parameter