Promoting Connectivity of Network-Like Structures by Enforcing Region Separation

We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. In simple terms, a gap in the predicted road causes two background regions, that lie on the opposite sides of a ground truth road, to touch in prediction. Our loss function is designed to prevent such unwanted connections between background regions, and therefore close the gaps in predicted roads. It also prevents predicting false positive roads and canals by penalizing unwarranted disconnections of background regions. In order to capture even short, dead-ending road segments, we evaluate the loss in small image crops. We show, in experiments on two standard road benchmarks and a new data set of irrigation canals, that convnets trained with our loss function recover road connectivity so well, that it suffices to skeletonize their output to produce state of the art maps. A distinct advantage of our approach is that the loss can be plugged in to any existing training setup without further modifications

Satellite soil moisture observations predict burned area in Southeast Asian peatlands

Fires that emit massive amounts of CO2 and particulate matter now burn with regularity in Southeast Asian tropical peatlands. Natural peatlands in Southeast Asia are waterlogged for most of the year and experience little or no fire, but networks of canals constructed for agriculture have drained vast areas of these peatlands, making the soil vulnerable to fire during periods of low rainfall. While soil moisture is the most direct measure of peat flammability, it has not been incorporated into fire studies due to an absence of regional observations. Here, we create the first remotely sensed soil moisture dataset for tropical peatlands in Sumatra, Borneo and Peninsular Malaysia by applying a new retrieval algorithm to satellite data from the Soil Moisture Active Passive (SMAP) mission with data spanning the 2015 El Ni&no burning event. Drier soil up to 30 days prior to fire correlates with larger burned area. The predictive information provided by soil moisture complements that of precipitation. Our remote sensing-derived results mirror those from a laboratory-based peat ignition study, suggesting that the dependence of fire on soil moisture exhibits scale independence within peatlands. Soil moisture measured from SMAP, a dataset spanning 2015-present, is a valuable resource for peat fire studies and warning systems. ©2019 The Author(s). Published by IOP Publishing Ltd.NASA Earth and Space Science Fellowship Program (Grant no. 80NSSC18K1341)NSF (EAR-1923478)NASA Terrestrial Ecology award (80NSSC18K0715)National Research Foundation (CREATE program)National Research Foundation (Grant No. NRF2016-ITCOO1-021

Climate change-induced peatland drying in Southeast Asia

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands

Drainage canals in Southeast Asian peatlands increase carbon emissions

Abstract Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long-term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced decades of land use change, the exact extent and spatial distribution of drainage canals are unknown. This has prevented regional-scale investigation of the relationships between drainage, land use, and carbon emissions. Here, we create the first regional map of drainage canals using high resolution satellite imagery and a convolutional neural network. We find that drainage is widespread—occurring in at least 65% of peatlands and across all land use types. Although previous estimates of peatland carbon emissions have relied on land use as a proxy for drainage, our maps show substantial variation in drainage density within land use types. Subsidence rates are 3.2 times larger in intensively drained areas than in non-drained areas, highlighting the central role of drainage in mediating peat subsidence. Accounting for drainage canals was found to improve a subsidence prediction model by 30%, suggesting that canals contain information about subsidence not captured by land use alone. Thus, our data set can be used to improve subsidence and associated carbon emissions predictions in peatlands, and to target areas for hydrologic restoration

Differential phase-shift-keying demodulation by coherent perfect absorption in silicon photonics.

We demonstrate a novel differential phase-shift-keying (DPSK) demodulator based on coherent perfect absorption (CPA). Our DPSK demodulator chip device, which incorporates a silicon ring resonator, two bus waveguide inputs, and monolithically integrated detectors, operates passively at a bit rate of 10 Gbps at telecommunication wavelengths, and fits within a mm-scale footprint. Critical coupling is used to achieve efficient CPA by tuning the gap between the ring and bus waveguides. The device has a vertical eye opening of 12.47 mV and a quality factor exceeding 3×1

Coherent-Perfect-Absorption-based DPSK Demodulator for Silicon Photonics

We demonstrate a fully integrated 10 Gbps novel Si DPSK demodulator using coherent perfect absorption. Our device incorporates a silicon ring resonator, two bus waveguide inputs, monolithically integrated detectors, and operates passively at telecommunication wavelengths, and fits within a mm-scale footprint